Many of the basic research problems we take for granted in computer graphics contain insidious assumptions about race. These troubling issues pre-date computer graphics, and can be traced back to the film technology and techniques from the analog era. Far from being incidental, they directly determine the physical formulations and numerical algorithms we use to depict virtual humans today. Instead of perpetuating the prejudices of previous eras, can we engage in anti-racist research that works to dismantle it?

This BOF is for attendees interested in discussing issues of racial bias that have long been embedded in computer graphics research. More than just an issue of workforce diversity, we will discuss discriminatory assumptions embedded in our community's basic technical formulations.

Overcoming these biases will require community-wide structural changes. Research that depicts all forms of humanity must be initiated, but also recognized as valid, non-niche, lines of inquiry. This will be a friendly, collaborative space for mutual, authentic engagement across difference. Attendees can be at any stage, including learning about, taking steps toward, or enacting change in computer graphics research.

We survey the treatment of sex and gender in the Computer Graphics research literature from an algorithmic fairness perspective. The established practices on the use of gender and sex in our community are scientifically incorrect and constitute a form of algorithmic bias with potential harmful effects. We propose ways of addressing these trends as technical limitations.

Digital avatars will be crucial components for immersive telecommunication, gaming, and the coming metaverse. Unfortunately, current methods for estimating the facial appearance (albedo) are biased to estimate light skin tones. This talk raises awareness of the problem with an analysis of (1) dataset biases and (2) the light/albedo ambiguity. We show how these problems can be ameliorated by recent advances, improving fairness in albedo estimation.

Science and Technology Studies (STS) is an academic interdisicpline that uses sociological and historical methods to study the interrelations of society and technoscience. This paper uses an STS approach to examine the historical feedback loops between "rendering" the shine and specularity of Black skin - across painting, video, and photography - and how computer graphics programmers and artists should question some of the fundamental assumptions of their rendering workflows to both create more equitable representation of human form, and also to understand how computational renderings influence the real world they represent.

Due to our proximity to industry pathways, VFX curriculums are good at mapping visible graduate attributes to core skills. Visible attributes are skills that can be measured via portfolio work and are reflected on student transcripts. Examples of such attributes may be building digital humans, creating physically accurate shaders, and designing story worlds. However, in order for the discipline of VFX to reflect our dynamic global culture and ensure equitable workplaces, we must also find ways to map graduate attributes to the values that drive technical and cultural diversity. Such attributes are harder to measure and can be understood as invisible attributes.

As we explore a broad range of characters and stories in our films, it has become increasingly valuable to view breakdowns of our character pools and selections by demographic: to build and use our assets efficiently, reinforce storytelling and world building choices, and ensure consistent decision-making across the pipeline. With the Character Linker App within Traction (Traction is Pixar's asset and shot-tracking tool), production is able to see a live breakdown of the character pool as assets are built, and sequence/shot composition, as they are populated with the ability to visualize by a range of categories, including gender, ethnicity, body-type, and age, among others. Each film can define and populate these categories specific to their story, set breakdown goals to measure progress against, and iterate on crowd asset selections to ensure each character is utilized to the fullest.

Inclusive Character Creator is a speculative design research project that seeks to address some of the long-standing issues of sexism, racism, ableism, and sizeism prevalent in most 3D character creators in interactive media. This project focuses on stylized and expressive features rather than hyperrealism. It seeks to redefine what it means to start a character from a "default body," a definition that usually results in creating a biased system that relies on media norms. A version 1.0 has been built, encapsulating fundamental principles resulting from research.

Animations have become increasingly realistic with the evolution of Computer Graphics (CG). In particular, human models and behaviors have been represented through animated virtual humans. Gender is a characteristic related to human identification, so virtual humans assigned to a specific gender have, in general, stereotyped representations through movements, clothes, hair, and colors in order to be understood by users as desired by designers. An important area of study is determining whether participants' perceptions change depending on how a virtual human is visually presented. Findings in this area can help the industry guide the modeling and animation of virtual humans to deliver the expected impact to the public. In this paper, we reproduce using an animated CG baby, a previous perceptual study conducted in real life aimed to assess gender bias about a baby. Our research indicates that simply textually reporting a virtual human's gender may be sufficient to create a perception of gender that affects the participant's emotional response so that stereotyped behaviors can be avoided.

This BOF is for attendees interested in discussing issues of racial bias embedded in computer graphics research. It is a follow-on from last year's BOF, "Countering Racial Bias in Computer Graphics Research Requires Structural Change", as well as this year's Talk, "Countering Racial Bias in Computer Graphics Research".

We will celebrate progress over the last year, discuss setbacks, and brainstorm paths forward. This will be a friendly, collaborative space for mutual, authentic engagement across difference. Attendees can be at any stage, including learning about, taking steps toward, or enacting change in computer graphics research.

We present an isotropic, hyperelastic model specifically designed for the efficient simulation of tightly coiled hairs whose curl radii approach 5 mm. Our model is robust to large bends and torsions, even when they appear at the scale of the strand discretization. The terms of our model are consistently quadratic with respect to their primary variables, do not require per-edge frames or any parallel transport operators, and can efficiently take large timesteps on the order of 1/30 of a second. Additionally, we show that it is possible to obtain fast, closed-form eigensystems for all the terms in the energy. Our eigenanalysis is sufficiently generic that it generalizes to other models. Our entirely vertex-based formulation integrates naturally with existing finite element codes, and we demonstrate its efficiency and robustness in a variety of scenarios.

Artists at Walt Disney Animation Studios have been constantly seeking to improve the physical depiction of an increasingly wider range of skin tones with each subsequent film. "Strange World" presented a great opportunity to improve on how we portray a wide range of skin types. The changes we implemented included: creating a new skin material from the ground up, changing the testing light rig to better evaluate materials, and implementing lighting strategies to better represent the skin properties of each character. What resulted was character skin that improved on our depiction of a wider range of skin types while maintaining a level of stylization in line with the art direction of the film.

In our analysis of gender diversity in graphics conference leadership, we examined the underrepresentation of non-male researchers, including women and non-binary individuals, in high-profile roles across key conferences from 2010 to 2023. Our study focused on Conference Chair, Papers Chair, and Keynote Speaker positions at six major conferences. Using perceived gender based on pronouns, we found 19% non-male representation among 543 leadership positions, with SIGGRAPH North America showing higher non-male participation. Our data also revealed a limited leadership circle with repeated roles filled by a small number of women, indicating a potential "diversity tax." We observed a trend of increasing non-male representation over time, though the pace is uncertain. Furthermore, our study suggests a correlation between the gender of conference chairs and keynote speakers, underscoring the importance of diverse leadership. We conclude that increasing non-male and particularly non-binary representation is crucial for reflecting societal and student demographics, yet we emphasize the need for a broader approach to address representational diversity and inclusivity in the graphics community.

Telling amazing stories involving beloved characters is at the forefront of what we do at Nickelodeon Animation Studio. It is imperative to tell these stories with characters from diverse backgrounds and experiences, as it is important to see the world through their eyes and perspective. We pride ourselves in our history of demonstrating diversity, equity, and inclusion in our shows, and continue to innovate our CG processes and workflows to keep DEI representation relevant in the broadcast space. Recent productions such as "Big Nate", "Transformers: EarthSpark", and "Monster High" exhibit our commitment to DEI. This panel will demonstrate the efforts, focus, and resoluteness we pursue on being inclusive and authentically representing our characters in our productions.

Computer Graphics has a long history in the design of effective algorithms for handling contact and friction between solid objects. For the sake of simplicity and versatility, most methods rely on low-order primitives such as line segments or triangles, both for the detection and the response stages. In this paper we carefully analyse, in the case of fibre systems, the impact of such choices on the retrieved contact forces. We highlight the presence of artifacts in the force response that are tightly related to the low-order geometry used for contact detection. Our analysis draws upon thorough comparisons between the high-order super-helix model and the low-order discrete elastic rod model. These reveal that when coupled to a low-order, segment-based detection scheme, both models yield spurious jumps in the contact force profile. Moreover, these artifacts are shown to be all the more visible as the geometry of fibres at contact is curved. In order to remove such artifacts we develop an accurate high-order detection scheme between two smooth curves, which relies on an efficient adaptive pruning strategy. We use this algorithm to detect contact between super-helices at high precision, allowing us to recover, in the range of wavy to highly curly fibres, much smoother force profiles during sliding motion than with a classical segment-based strategy. Furthermore, we show that our approach offers better scaling properties in terms of efficiency vs. precision compared to segment-based approaches, making it attractive for applications where accurate and reliable forces are desired. Finally, we demonstrate the robustness and accuracy of our fully high-order approach on a challenging hair combing scenario.

We examine the life and legacy of pioneering Vietnamese computer scientist Búi Tướng Phong, whose shading and lighting models turned 50 last year. We trace the trajectory of his life through Vietnam, France, and the United States, and its intersections with global conflicts. Crucially, we present definitive evidence that his name has been cited incorrectly over the last five decades. His family name is Búi Tướng, not Phong. By presenting these facts at SIGGRAPH, we hope to collect more information about his life, and ensure that his name is remembered correctly in the future.

We will cover recent advances and ongoing challenges in the depiction of Black hair, otherwise known as kinky, or Afro-textured hair. In computer graphics, the majority hair research has been in the depiction straight or wavy hair. As a result, many aspects of the aesthetics and mechanics of Black hair remain poorly understood. To help fill this gap, we will present Code My Crown, a free guide to creating Black digital hairstyles that we co-authored in collaboration with a community of game artists and Dove. We also cover styling guidelines for 3D models in the Open Source Afro Hair Library, and present Lifted Curls, our strand simulation technique specifically designed for Afro-textured hair. Finally, we will suggest future directions for hair research.

Stylized avatars used in hybrid telepresence must support the large diversity in human appearance existing in the world. Most avatar systems assume a generic base rig that morphs into varied face and body shapes. However, retrofitting shapes for under-represented populations as an afterthought is costly. We need new avatar systems, visual languages, and human understanding technology, all co-designed with diversity in mind from the start.

This BOF is for attendees interested in discussing issues of racial bias embedded in computer graphics research. It is a follow-on from the SIGGRAPH 2021 BOF, "Countering Racial Bias in Computer Graphics Research Requires Structural Change", as well as the SIGGRAPH 2022 Talk and BOF, "Countering Racial Bias in Computer Graphics Research". We will celebrate progress over the last three years, discuss setbacks, and brainstorm paths forward. This will be a friendly, collaborative space for mutual, authentic engagement across difference. Attendees can be at any stage, including learning about, taking steps toward, or enacting change in computer graphics research.

We present a method for prediction of a person's hairstyle from a single image. Despite growing use cases in user digitization and enrollment for virtual experiences, available methods are limited, particularly in the range of hairstyles they can capture. Human hair is extremely diverse and lacks any universally accepted description or categorization, making this a challenging task. Most current methods rely on parametric models of hair at a strand level. These approaches, while very promising, are not yet able to represent short, frizzy, coily hair and gathered hairstyles. We instead choose a classification approach which can represent the diversity of hairstyles required for a truly robust and inclusive system. Previous classification approaches have been restricted by poorly labeled data that lacks diversity, imposing constraints on the usefulness of any resulting enrollment system. We use only synthetic data to train our models. This allows for explicit control of diversity of hairstyle attributes, hair colors, facial appearance, poses, environments and other parameters. It also produces noise-free ground-truth labels. We introduce a novel hairstyle taxonomy developed in collaboration with a diverse group of domain experts which we use to balance our training data, supervise our model, and directly measure fairness. We annotate our synthetic training data and a real evaluation dataset using this taxonomy and release both to enable comparison of future hairstyle prediction approaches. We employ an architecture based on a pre-trained feature extraction network in order to improve generalization of our method to real data and predict taxonomy attributes as an auxiliary task to improve accuracy. Results show our method to be significantly more robust for challenging hairstyles than recent parametric approaches.

We present geometric methods for generating shapes that are characteristic of highly coiled hair. Different features become visually relevant when hairs are well-approximated by high-frequency helices instead of low-frequency curves, so we present algorithms for three such phenomena. First, a Fourier-based method for phase locking, the process by which disparate helices near the scalp coalesce into a single curl. Second, a method for period skipping which models individual helices deviating from the coalesced curl. Third, a non-linear optimization that directly generates the shapes of switchbacks, a.k.a. helical perversions, which heretofore could only be produced through direct physical simulation. By applying all three methods in tandem, we show that we can achieve richly detailed depictions of highly coiled hair.

We surveyed 888 SIGGRAPH papers from 2018-2024 and gathered author-reported GPU models. By contextualizing the hardware reported in papers with available data of consumers' hardware, we demonstrate that research is consistently developed and tested on new high-end devices that do not reflect the state of the consumer-level market.

Rendering Virtual Humans (VHs) with realistic and equitable skin tones across diverse populations remains a significant challenge. Current computer graphics techniques and tools often exhibit inherent biases that favor lighter skin tones. This can lead to VHs with darker skin appearing less lifelike, poorly detailed, or inaccurately lit, diminishing inclusivity and the user's sense of presence in digital experiences. As VHs become increasingly integral to games, films, virtual reality, and metaverse applications, a systematic understanding of how rendering pipelines handle skin tone diversity is crucial to ensure fair and accurate digital representation for everyone.

This BOF is for attendees interested in discussing issues of racial bias embedded in computer graphics research. It is a follow-on from similarly titled BOFs at SIGGRAPH 2021, 2022, and 2024. We will celebrate progress over the last four years, discuss setbacks, and brainstorm paths forward. This will be a friendly, collaborative space for mutual, authentic engagement across difference. Attendees can be at any stage, including learning about, taking steps toward, or enacting change in computer graphics research.

The talk presents fast, modular approximations of Curly-Cue's methods for kinky-coily hair-switchbacks, phase locking, and period skipping-integrated into Netflix Animation Studio's Houdini-based grooming pipeline, prioritizing artistic control, simplicity, and interactivity over strict physical accuracy while achieving natural-looking hairstyles.

For Disney/Pixar's Toy Story 5 character Blaze, we developed a grooming structure for tightly coiled curls, enabling rapid artistic iteration. We deployed Fizt Strands, built on our Fizt cloth solver, offering superior hair collision and stability. These advancements enabled believable curl simulation, complimenting the character's ethnicity and personality.

We introduce SIGGRAPH to critical theories of generative models, focusing on socioeconomic relations to research and art. We introduce critical theory through computing's relationship to commodification and labor and contrast two views of generative models: one as the automation of labor and another examining their role in altering human sociality.

Due to its highly curved geometry, tightly coiled hair is challenging to model and edit using standard position-based tools. In this work we propose using material curvatures and twists to analyze and edit tightly coiled hair styles. Our method relies on the geometry of super-helices, primitives parametrized by piecewise constant curvatures and twists, whose helical geometry naturally resembles a coiled hair strand. Using this curvature/twist space, we introduce new editing tools that allow us to expand, shrink, "ruffle", interpolate or guide the position of coiled hair in a natural way. We present analytical expressions for geometry and gradients that allow our method to run efficiently and without the need for any training data. We successfully apply our tools to highly coiled simulated hairs, as well as those generated procedurally.

Computer graphics algorithms for generating photorealistic imagery are widely perceived to be universal, and capable of conjuring anything that a filmmaker or game designer can imagine. However, recent works have suggested that 3D algorithms for depicting synthetic humans are far from generic, and instead favor historically hegemonic characteristics. We present the first systematic review of human depiction in the top computer graphics conference and the journal of record (SIGGRAPH and ACM Transactions on Graphics) that confirms previous hypotheses. Algorithms that claim to be generically rendering "human skin" are in fact imagined and formulated for translucent, "high albedo" materials such as white skin. Algorithms claiming to apply generically to "human hair" are formulated for "rods," "wires" and "threads" which are analogous to straight hair. Our analysis reveals conceptual binarization, where algorithms for white skin are treated as computational substrate for "all" skin, imposing a hierarchical assumption that all skin descends from the math and physics of white skin. Hair algorithms follow a similar historical pattern, with the first examples of computer-generated Type 4 hair only appearing after the murder of George Floyd in 2020. We offer a new conceptual label, McDaniels Methods, for characterizing and critiquing computer graphics algorithms that reinforce racial hierarchy under a false cover of diversity. We also offer an inverse label, Durald Methods, for algorithms that were closely co-designed with the people being depicted. Our analysis points the way towards several neglected avenues for future research.

We invite researchers and artists to discuss co-designing computer graphics technologies. Building collaborations between researchers and artists is essential to designing tools for visual production that use novel techniques to support human creative processes rather than supplant or degrade them. Drawing from the organizers' experience in designing creative software in academic and industry settings, we will discuss concrete methods to build mutually beneficial collaborations with creative practitioners. We see co-design as a way to simultaneously foster scientific contributions and powerful creative workflows for artists, and more broadly as a way to collectively define meaningful directions for innovation in the field.

From climate change to biodiversity loss and resource exhaustion, human activities are impacting Earth's limits and Computer Graphics is no exception. How can our research practices and organizations evolve to respect these limits?

After the success of last year's BoF, we continue building a community of people who want to think about the broader impacts of our research and how to collectively work towards a more sustainable future.

This interactive meetup session will allow attendees to share experiences and questions on related topics in small groups, regardless of their current levels of involvement or expertise in sustainable approaches to research.

This BOF is for attendees interested in discussing racial bias and related issues embedded in computer graphics research. It is a follow-on to similarly titled BOFs at SIGGRAPH 2021, 2022, 2024, and 2025. We will celebrate progress over the last five years, discuss setbacks, and brainstorm paths forward. This will be a friendly, collaborative space for mutual, authentic engagement across difference. Attendees can be at any stage, including learning about, taking steps toward, or enacting change in computer graphics research.