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PhySimCode: A Benchmark and Evaluation Method for Physics Video to Code Generation
Souraja Kundu, Aditya Gupta, Joel Julin +3
Multimodal large language models (MLLMs) are good at video understanding and code generation, but it remains unclear whether they can recover the physical laws underlying videos, a…
MAD-HOI: Masked Autoregressive Diffusion for Generating Articulated Hand Object Interactions from Text
Ananya Bal, Kartik Sharma, Ethan Lai +4
Methods for text-based generation of hand-object interaction (HOI) sequences primarily focus on producing smooth, physically plausible trajectories. A truly utilitarian method shou…
JointHOI: Jointly Generating Contact Maps Enhances Hand Object Interaction Generation
Mingyeong Song, Jungbin Cho, Jisoo Kim +5
Text driven hand object interaction (HOI) generation is gaining attention for immersive applications and robotics, yet producing physically plausible interactions remains challengi…
GeoStream: Toward Precise Camera Controlled Streaming Video Generation
Yizhou Zhao, Yifan Wang, Xiaoyuan Wang +11
Accurate interactive camera control is essential for video-based world models, but most existing approaches learn camera motion implicitly, leading to inaccurate control under out-…
MOSIV: Multi-Object System Identification from Videos
Chunjiang Liu, Xiaoyuan Wang, Qingran Lin +9
We introduce the challenging problem of multi-object system identification from videos, for which prior methods are ill-suited due to their focus on single-object scenes or discret…
SkipSR: Faster Super Resolution with Token Skipping
Rohan Choudhury, Shanchuan Lin, Jianyi Wang +6
Diffusion-based super-resolution (SR) is a key component in video generation and video restoration, but is slow and expensive, limiting scalability to higher resolutions and longer…
Accelerating Vision Transformers with Adaptive Patch Sizes
Rohan Choudhury, JungEun Kim, Jinhyung Park +3
Vision Transformers (ViTs) partition input images into uniformly sized patches regardless of their content, resulting in long input sequence lengths for high-resolution images. We…
Don't Look Twice: Faster Video Transformers with Run-Length Tokenization
Rohan Choudhury, Guanglei Zhu, Sihan Liu +3
Transformers are slow to train on videos due to extremely large numbers of input tokens, even though many video tokens are repeated over time. Existing methods to remove such uninf…