1 citations · 2 across the 10 of their papers we have counts for
11 papers
LuxDiT: Lighting Estimation with Video Diffusion Transformer
Ruofan Liang, Kai He, Zan Gojcic +4
Estimating scene lighting from a single image or video remains a longstanding challenge in computer vision and graphics. Learning-based approaches are constrained by the scarcity o…
UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting
Kai He, Ruofan Liang, Jacob Munkberg +7
We address the challenge of relighting a single image or video, a task that demands precise scene intrinsic understanding and high-quality light transport synthesis. Existing end-t…
Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions
David Acuna, Ximing Lu, Jaehun Jung +4
Recent research in vision-language models (VLMs) has centered around the possibility of equipping them with implicit long-form chain-of-thought reasoning -- akin to the success obs…
Controllable Weather Synthesis and Removal with Video Diffusion Models
Chih-Hao Lin, Zian Wang, Ruofan Liang +4
Generating realistic and controllable weather effects in videos is valuable for many applications. Physics-based weather simulation requires precise reconstructions that are hard t…
LongPerceptualThoughts: Distilling System-2 Reasoning for System-1 Perception
Yuan-Hong Liao, Sven Elflein, Liu He +4
Recent reasoning models through test-time scaling have demonstrated that long chain-of-thoughts can unlock substantial performance boosts in hard reasoning tasks such as math and c…
Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models
Yuan-Hong Liao, Rafid Mahmood, Sanja Fidler +1
Despite recent advances demonstrating vision-language models' (VLMs) abilities to describe complex relationships in images using natural language, their capability to quantitativel…
Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering
Ruofan Liang, Zan Gojcic, Merlin Nimier-David +4
The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formatio…
3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes
Nicolas Moenne-Loccoz, Ashkan Mirzaei, Or Perel +6
Particle-based representations of radiance fields such as 3D Gaussian Splatting have found great success for reconstructing and re-rendering of complex scenes. Most existing method…
RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting
Ashkan Mirzaei, Riccardo De Lutio, Seung Wook Kim +5
Neural reconstruction approaches are rapidly emerging as the preferred representation for 3D scenes, but their limited editability is still posing a challenge. In this work, we pro…
Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?
Yuan-Hong Liao, Rafid Mahmood, Sanja Fidler +1
Enhancing semantic grounding abilities in Vision-Language Models (VLMs) often involves collecting domain-specific training data, refining the network architectures, or modifying th…
Augmented Reality based Simulated Data (ARSim) with multi-view consistency for AV perception networks
Aqeel Anwar, Tae Eun Choe, Zian Wang +2
Detecting a diverse range of objects under various driving scenarios is essential for the effectiveness of autonomous driving systems. However, the real-world data collected often…