1 citations · 2 across the 7 of their papers we have counts for
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IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
Yitong Chen, Zijie Diao, Junke Wang +5
Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing semantically rich latent spac…
All-in-One Conditioning for Text-to-Image Synthesis
Hirunima Jayasekara, Chuong Huynh, Yixuan Ren +2
Accurate interpretation and visual representation of complex prompts involving multiple objects, attributes, and spatial relationships is a critical challenge in text-to-image synt…
Characterizing Motion Encoding in Video Diffusion Timesteps
Vatsal Baherwani, Yixuan Ren, Abhinav Shrivastava
Text-to-video diffusion models synthesize temporal motion and spatial appearance through iterative denoising, yet how motion is encoded across timesteps remains poorly understood.…
NeRV-Diffusion: Diffuse Implicit Neural Representations for Video Synthesis
Yixuan Ren, Hanyu Wang, Hao Chen +2
We present NeRV-Diffusion, an implicit latent video diffusion model that synthesizes videos via generating neural network weights. The generated weights can be rearranged as the pa…
LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior
Hanyu Wang, Saksham Suri, Yixuan Ren +2
We present LARP, a novel video tokenizer designed to overcome limitations in current video tokenization methods for autoregressive (AR) generative models. Unlike traditional patchw…
Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models
Yixuan Ren, Yang Zhou, Jimei Yang +5
Image customization has been extensively studied in text-to-image (T2I) diffusion models, leading to impressive outcomes and applications. With the emergence of text-to-video (T2V)…
Towards Scalable Neural Representation for Diverse Videos
Bo He, Xitong Yang, Hanyu Wang +6
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e.g., NeRV, E-NeRV). W…