Sauradip Nag

Thanks for sharing @janusch_patas ! TL;DR - Sparse Views to Dynamic 3D generation. Key Insight - Slowing a video (magnifying temporally) improves 3D generation quality.

📢📢📢introducing 𝐏𝐨𝐰𝐞𝐫 𝐅𝐨𝐚𝐦 A 3D representation that can be ray traced or rasterized in real time, with NO COMPROMISE in quality. - Project: powerfoam.github.io - arXiv: arxiv.org/abs/2604.24994 Rasterized at 3DGS-class FPS Ray traced at Radiant Foam speeds

Video Analysis and Generation via a Semantic Progress Function paper: huggingface.co/papers/2604.22…

Shared attention is a powerful way to transfer style in diffusion models: let tokens attend to a reference image, and the model can pick up stylistic cues. ⚠️ But in RoPE-based DiTs, this often breaks badly. Instead of transferring style, the model starts copying the reference content. In this work, Untwisting RoPE, we explore how positional bias and semantic understanding become twisted together in DiTs, and how frequency control can untwist them.

Excited to share our recent work: Free-Range Gaussians 🥚✨ The core idea: instead of predicting Gaussians on a pixel- or voxel-aligned grid, we let them live freely in 3D space. 🌐 Project: free-range-gaussians.github.io 📝 Paper: arxiv.org/abs/2604.04874

Exciting progress in the auto-seam & auto-UV space!🔥 Thrilled to share our recent work MeshTailor. Huge congrats to @qixuema for driving this incredible project. We finally brought AI to native seam generation, learning directly from professional data. Check the video below!

📢📢📢 Introducing "FullCircle: Effortless 3D Reconstruction from Casual 360° Captures" TL;DR: 10x faster casual capture with clean reconstructions Homepage: theialab.github.io/fullcircle Code: github.com/theialab/fullc… arXiv: arxiv.org/abs/2603.22572 Led by Yalda Foroutan & Ipek Oztas

@taiyasaki @sherwinbahmani Congrats @sherwinbahmani 🔥

Congratulations Dr. @sherwinbahmani

@janheld14 @3DVconf Congrats Jan. Finally it got a home 🎊🎉

Triangle Splatting has been accepted at @3DVconf! Big congrats to the whole team!🔥

Advances in 4D Representation: Geometry, Motion, and Interaction Mingrui Zhao, @Dumb_Thug, Kai Wang, @anvorain, Guangda Ji, Peter Chun, @arash_mham, @richardzhangsfu tl;dr: in title arxiv.org/abs/2510.19255

Advances in 4D Representation: Geometry, Motion, and Interaction Abstract (excerpt) Instead of offering an exhaustive enumeration of many works, we take a more selective approach by focusing on representative works to highlight both the desirable properties and ensuing challenges of each representation under different computation, application, and data scenarios. The main take-away message we aim to convey to the readers is how to select and then customize the appropriate 4D representations for their tasks. Organizationally, we separate the 4D representations based on three key pillars: geometry, motion, and interaction. Our discourse will not only encompass the most popular representations of today, such as neural radiance fields (NeRFs) and 3D Gaussian Splatting (3DGS), but also bring attention to relatively under-explored representations in the 4D context, such as structured models and long-range motions. Throughout our survey, we will reprise the role of large language models (LLMs) and video foundational models (VFMs) in a variety of 4D applications, while steering our discussion towards their current limitations and how they can be addressed. We also provide dedicated coverage on what 4D datasets are currently available, as well as what is lacking, to drive the subfield forward.

📢📢📢 "ASIA" @ #SIGGRAPHAsia2025. 😉 We segment 3D shapes into possibly non-semantic and non-text describable parts using only a few annotated in-the-wild images as references! 👉Project Page: sairajk.github.io/asia/ 📄Paper: arxiv.org/abs/2509.24288

Check out my first PhD paper!

🚀 Introducing PhysiX: One of the first large-scale foundation models for physics simulations! PhysiX is a 4.5B parameter model that unifies a wide range of physical systems, from fluid dynamics to reaction-diffusion, outperforming specialized, state-of-the-art models.

Quick “teaser” for a fun #SIGGRAPH2025 project, led by Hossein Baktash, on optimizing a shape to have the desired rolling statistics. Basically we can turn arbitrary objects into fair dice, or make dice which capture the statistics of other objects—like several coin flips.

Excited to share that TokenVerse won Best Paper Award at SIGGRAPH 2025! 🎉 TokenVerse enables personalization of complex visual concepts, from objects and materials to poses and lighting, each can be extracted from a single image and be recomposed into a coherent result. 👇

🚀🚀🚀 Blazing-fast non-rigid image editing in 2 seconds🔥!! Inspired by Multi-View 3D Correspondence, our #SIGGRAPH2025 work uses semantic correspondence in the Diffusion latent for complex image editing tasks, achieving lightning-fast⚡️results. Check out our demo !! 🔎🔎

Just saw this forwarded from @DanielCohenOr1 We wrote a @siggraph paper in 2016: cs.sfu.ca/~haoz/pubs/zou… The following "Calligram Animals" were generated automatically by our algorithm. Which ones are better? I feel that the GPT4o results are a bit mechanical ... less "artistic."

Professor Richard Zhang has been inducted into the @TheOfficialACM's @siggraph Academy! This prestigious recognition honors individuals who have made outstanding contributions to the field of computer graphics and interactive techniques. Congrats, Richard! ow.ly/bp5V50VYSpn

🚀 Struggling with the lack of high-quality data for AI-driven human-object interaction research? We've got you covered! Introducing HUMOTO, a groundbreaking 4D dataset for human-object interaction, developed with a combination of wearable motion capture, SOTA 6D pose estimation vision models, LLM, and the professional refining works of multiple animation studios. HUMOTO features: ✅ Over 700 diverse daily activities ✅ Interactions with 60+ objects, 70+ articulated parts. ✅ Fine-grained text annotations ✅ Detailed hand and finger movements We hope HUMOTO will fuel your Humanoid AI research and drive new advancements! For research or commercial license inquiries, please contact yizho@adobe.com. Explore the dataset: 👉 HUMOTO Dataset Website adobe-research.github.io/humoto/ Learn more: 👉 HUMOTO Project Page jiaxin-lu.github.io/humoto/ @jacinth_lu @qixing_huang #AI #HumanObjectInteraction #HumanoidAI #MotionCapture #HUMOTO #AdobeResearch

Happy to support the #3DV2026 publicity efforts!