88 citations · 141 across the 12 of their papers we have counts for
13 papers
Growing Visual Generative Capacity for Pre-Trained MLLMs
Hanyu Wang, Jiaming Han, Ziyan Yang +6
Multimodal large language models (MLLMs) extend the success of language models to visual understanding, and recent efforts have sought to build unified MLLMs that support both unde…
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…
Fast Encoding and Decoding for Implicit Video Representation
Hao Chen, Saining Xie, Ser-Nam Lim +1
Despite the abundant availability and content richness for video data, its high-dimensionality poses challenges for video research. Recent advancements have explored the implicit r…
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…
HNeRV: A Hybrid Neural Representation for Videos
Hao Chen, Matt Gwilliam, Ser-Nam Lim +1
Implicit neural representations store videos as neural networks and have performed well for various vision tasks such as video compression and denoising. With frame index or positi…
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…