#siggraph2026

Anya Rossi

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Introducing InfiniteDiffusion, my independent paper accepted to #SIGGRAPH2026! I have one RTX 3090 Ti. No funding, advisors, or team. By day I'm a new grad SWE at Walmart. The paper has two main contributions: - InfiniteDiffusion: a new approach to infinite generation with diffusion models. - Terrain Diffusion: the world’s first learned procedural terrain generator. Here’s why this matters, and how they are connected. 🧵

Introducing InfiniteDiffusion, my independent paper accepted to #SIGGRAPH2026! I have one RTX 3090 Ti. No funding, advisors, or team. By day I'm a new grad SWE at Walmart. The paper has two main contributions: - InfiniteDiffusion: a new approach to infinite generation with diffusion models. - Terrain Diffusion: the world’s first learned procedural terrain generator. Here’s why this matters, and how they are connected. 🧵

Alexander Goslin

933,729 views • 1 month ago

Missed the NVIDIA keynote at #SIGGRAPH2026? Catch up on the latest breakthroughs in neural rendering, world models and simulation for robotics, presented by NVIDIA research and engineering leaders Neil Ashton, Edward Liu and Ming-Yu Liu. Watch on demand 🎥

Missed the NVIDIA keynote at #SIGGRAPH2026? Catch up on the latest breakthroughs in neural rendering, world models and simulation for robotics, presented by NVIDIA research and engineering leaders Neil Ashton, Edward Liu and Ming-Yu Liu. Watch on demand 🎥

92,336 views • 6 days ago

Come to our #SIGGRAPH2026 talk today: Our method turns existing 3D objects into multi-touch sensors, without modifying the interior. From a scan, we sample surface-intrinsic curves that conform to mutual-cap layouts, and optimize their intersections for coverage & fabrication.

Come to our #SIGGRAPH2026 talk today: Our method turns existing 3D objects into multi-touch sensors, without modifying the interior. From a scan, we sample surface-intrinsic curves that conform to mutual-cap layouts, and optimize their intersections for coverage & fabrication.

44,874 views • 5 days ago

3D scene reconstruction works great until the camera never sees part of the scene. ArtiFixer from NVIDIA Research is an open autoregressive model that fills in the missing geometry that other methods leave blank. #SIGGRAPH2026 paper, code + demo:

3D scene reconstruction works great until the camera never sees part of the scene. ArtiFixer from NVIDIA Research is an open autoregressive model that fills in the missing geometry that other methods leave blank. #SIGGRAPH2026 paper, code + demo:

154,156 views • 1 month ago

📢 Eyeline and Netflix latest research, 🌟Go-with-the-Track🌟, has been accepted to #SIGGRAPH2026. We propose a video generation framework that jointly conditions on multiple reference images and reference-anchored point tracks, enabling a range of content creation applications within a unified model: • Motion-preserving video restylization using multiple reference images; • Mesh- or keypoint-driven compositing and stylization; • Camera motion control for both static and dynamic scenes using multiple reference images captured from different view-points and time steps. ✊Congrats to the team: Koichi Namekata, Yash Kant, Zhizheng Liu, Ryan Burgert, Yuancheng Xu, Jordan Lin, Emmett Steven, Julien Philip, LiMa, Andrea Vedaldi, Paul Debevec, Ning Yu 📰 Paper: 🌐 Project: ⌨️ Code: *** This work is part of the ongoing research and development at Eyeline and Netflix, and we look forward to seeing its techniques and workflows adopted in future productions.

📢 Eyeline and Netflix latest research, 🌟Go-with-the-Track🌟, has been accepted to #SIGGRAPH2026. We propose a video generation framework that jointly conditions on multiple reference images and reference-anchored point tracks, enabling a range of content creation applications within a unified model: • Motion-preserving video restylization using multiple reference images; • Mesh- or keypoint-driven compositing and stylization; • Camera motion control for both static and dynamic scenes using multiple reference images captured from different view-points and time steps. ✊Congrats to the team: Koichi Namekata, Yash Kant, Zhizheng Liu, Ryan Burgert, Yuancheng Xu, Jordan Lin, Emmett Steven, Julien Philip, LiMa, Andrea Vedaldi, Paul Debevec, Ning Yu 📰 Paper: 🌐 Project: ⌨️ Code: *** This work is part of the ongoing research and development at Eyeline and Netflix, and we look forward to seeing its techniques and workflows adopted in future productions.

23,561 views • 1 month ago

This January, I decided to give it a shot and wrote my first paper Today, I am happy to share that it was accepted by #SIGGRAPH2026 SAD is a differentiable image representation with soft, anisotropic partitioning, with up to 20x faster encoding time🧵

This January, I decided to give it a shot and wrote my first paper Today, I am happy to share that it was accepted by #SIGGRAPH2026 SAD is a differentiable image representation with soft, anisotropic partitioning, with up to 20x faster encoding time🧵

34,665 views • 3 months ago

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