209 citations · 264 across the 23 of their papers we have counts for
31 papers · 1 filter
Human detectors are surprisingly powerful reward models
Kumar Ashutosh, XuDong Wang, Xi Yin +4
Video generation models have recently achieved impressive visual fidelity and temporal coherence. Yet, they continue to struggle with complex, non-rigid motions, especially when sy…
Toward Diffusible High-Dimensional Latent Spaces: A Frequency Perspective
Bolin Lai, Xudong Wang, Saketh Rambhatla +4
Latent diffusion has become the default paradigm for visual generation, yet we observe a persistent reconstruction-generation trade-off as latent dimensionality increases: higher-c…
Diffusion Autoencoders are Scalable Image Tokenizers
Yinbo Chen, Rohit Girdhar, Xiaolong Wang +2
Tokenizing images into compact visual representations is a key step in learning efficient and high-quality image generative models. We present a simple diffusion tokenizer (DiTo) t…
LLMs can see and hear without any training
Kumar Ashutosh, Yossi Gandelsman, Xinlei Chen +2
We present MILS: Multimodal Iterative LLM Solver, a surprisingly simple, training-free approach, to imbue multimodal capabilities into your favorite LLM. Leveraging their innate ab…
MotiF: Making Text Count in Image Animation with Motion Focal Loss
Shijie Wang, Samaneh Azadi, Rohit Girdhar +3
Text-Image-to-Video (TI2V) generation aims to generate a video from an image following a text description, which is also referred to as text-guided image animation. Most existing m…
Human Action Anticipation: A Survey
Bolin Lai, Sam Toyer, Tushar Nagarajan +7
Predicting future human behavior is an increasingly popular topic in computer vision, driven by the interest in applications such as autonomous vehicles, digital assistants and hum…