18 citations · 56 across the 20 of their papers we have counts for
8 papers · 1 filter
GenDeF: Learning Generative Deformation Field for Video Generation
Wen Wang, Kecheng Zheng, Qiuyu Wang +5
We offer a new perspective on approaching the task of video generation. Instead of directly synthesizing a sequence of frames, we propose to render a video by warping one static im…
Learning Naturally Aggregated Appearance for Efficient 3D Editing
Ka Leong Cheng, Qiuyu Wang, Zifan Shi +5
Neural radiance fields, which represent a 3D scene as a color field and a density field, have demonstrated great progress in novel view synthesis yet are unfavorable for editing du…
Gaussian Shell Maps for Efficient 3D Human Generation
Rameen Abdal, Wang Yifan, Zifan Shi +6
Efficient generation of 3D digital humans is important in several industries, including virtual reality, social media, and cinematic production. 3D generative adversarial networks…
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model
Yinghao Xu, Hao Tan, Fujun Luan +8
We propose \textbf{DMV3D}, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model inco…
Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis
Jiapeng Zhu, Ceyuan Yang, Kecheng Zheng +3
Due to the difficulty in scaling up, generative adversarial networks (GANs) seem to be falling from grace on the task of text-conditioned image synthesis. Sparsely-activated mixtur…
Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase
Qiuyu Wang, Zifan Shi, Kecheng Zheng +3
Despite the rapid advance of 3D-aware image synthesis, existing studies usually adopt a mixture of techniques and tricks, leaving it unclear how each part contributes to the final…
Learning 3D-aware Image Synthesis with Unknown Pose Distribution
Zifan Shi, Yujun Shen, Yinghao Xu +5
Existing methods for 3D-aware image synthesis largely depend on the 3D pose distribution pre-estimated on the training set. An inaccurate estimation may mislead the model into lear…
LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis
Jiapeng Zhu, Ceyuan Yang, Yujun Shen +4
This work presents an easy-to-use regularizer for GAN training, which helps explicitly link some axes of the latent space to a set of pixels in the synthesized image. Establishing…