2 citations · 6 across the 12 of their papers we have counts for
13 papers
Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach
Yunuo Chen, Junli Cao, Vidit Goel +5
We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D point trajectories and ali…
SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training
Dongting Hu, Jierun Chen, Xijie Huang +16
Existing text-to-image (T2I) diffusion models face several limitations, including large model sizes, slow runtime, and low-quality generation on mobile devices. This paper aims to…
Wonderland: Navigating 3D Scenes from a Single Image
Hanwen Liang, Junli Cao, Vidit Goel +6
How can one efficiently generate high-quality, wide-scope 3D scenes from arbitrary single images? Existing methods suffer several drawbacks, such as requiring multi-view data, time…
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation
Anil Kag, Huseyin Coskun, Jierun Chen +5
Neural network architecture design requires making many crucial decisions. The common desiderata is that similar decisions, with little modifications, can be reused in a variety of…
MaskControl: Spatio-Temporal Control for Masked Motion Synthesis
Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Korrawe Karunratanakul +7
Recent advances in motion diffusion models have enabled spatially controllable text-to-motion generation. However, these models struggle to achieve high-precision control while mai…
Lightweight Predictive 3D Gaussian Splats
Junli Cao, Vidit Goel, Chaoyang Wang +6
Recent approaches representing 3D objects and scenes using Gaussian splats show increased rendering speed across a variety of platforms and devices. While rendering such representa…
4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models
Heng Yu, Chaoyang Wang, Peiye Zhuang +6
Existing dynamic scene generation methods mostly rely on distilling knowledge from pre-trained 3D generative models, which are typically fine-tuned on synthetic object datasets. As…
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model
Yang Sui, Yanyu Li, Anil Kag +7
Diffusion-based image generation models have achieved great success in recent years by showing the capability of synthesizing high-quality content. However, these models contain a…
SF-V: Single Forward Video Generation Model
Zhixing Zhang, Yanyu Li, Yushu Wu +9
Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process. However, these models requir…
AToM: Amortized Text-to-Mesh using 2D Diffusion
Guocheng Qian, Junli Cao, Aliaksandr Siarohin +12
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously. In contrast to existing text-to-3D methods…
Diffusion Priors for Dynamic View Synthesis from Monocular Videos
Chaoyang Wang, Peiye Zhuang, Aliaksandr Siarohin +4
Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos. Existing methods struggle to distinguishing between motion and structure, parti…
LightSpeed: Light and Fast Neural Light Fields on Mobile Devices
Aarush Gupta, Junli Cao, Chaoyang Wang +4
Real-time novel-view image synthesis on mobile devices is prohibitive due to the limited computational power and storage. Using volumetric rendering methods, such as NeRF and its d…
Real-Time Neural Light Field on Mobile Devices
Junli Cao, Huan Wang, Pavlo Chemerys +6
Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to th…