26 citations · 42 across the 13 of their papers we have counts for
14 papers
RASA: Disentangled Spatial-Motional Priors for Cross-Identity Character Animation
Zhen Xiao, Zhen Shen, Zhaofan Qiu +3
Cross-identity character animation aims to drive a target identity from a reference image to follow the motion of a source character from a driving video. The core challenge lies i…
LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models
Zhenhao Shen, Jiaqi Liang, Jasper Lu +11
Human video is playing an increasingly central role in training World Action Models (WAMs), owing to its diversity and low collection cost relative to teleoperated robot data. Howe…
DSA: Dynamic Step Allocation for Fast Autoregressive Video Generation
Thanh-Tung Le, Yunhan Zhao, Menglei Chai +5
Video diffusion transformers have achieved state-of-the-art visual quality, but their high inference cost remains a major bottleneck for real-time applications. Recent distillation…
RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation
Feng Jiang, Yang Chen, Kyle Xu +8
Recent advances in large-scale video world models have enabled increasingly realistic future prediction, raising the prospect of using generated videos as scalable supervision for…
HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer
Qi Cai, Jingwen Chen, Chengmin Gao +22
The evolution of visual generative models has long been constrained by fragmented architectures relying on disjoint text encoders and external VAEs. In this report, we present HiDr…
From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation
Jasper Lu, Zhenhao Shen, Yuanfei Wang +8
Learning robust robot policies in real-world environments requires diverse data augmentation, yet scaling real-world data collection is costly due to the need for acquiring physica…
Wan-Animate: Unified Character Animation and Replacement with Holistic Replication
Gang Cheng, Xin Gao, Li Hu +23
We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by preci…
Wan-S2V: Audio-Driven Cinematic Video Generation
Xin Gao, Li Hu, Siqi Hu +20
Current state-of-the-art (SOTA) methods for audio-driven character animation demonstrate promising performance for scenarios primarily involving speech and singing. However, they o…
Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance
Li Hu, Guangyuan Wang, Zhen Shen +6
Recent character image animation methods based on diffusion models, such as Animate Anyone, have made significant progress in generating consistent and generalizable character anim…
Compact Real-time Radiance Fields with Neural Codebook
Lingzhi Li, Zhongshu Wang, Zhen Shen +2
Reconstructing neural radiance fields with explicit volumetric representations, demonstrated by Plenoxels, has shown remarkable advantages on training and rendering efficiency, whi…
4K-NeRF: High Fidelity Neural Radiance Fields at Ultra High Resolutions
Zhongshu Wang, Lingzhi Li, Zhen Shen +2
In this paper, we present a novel and effective framework, named 4K-NeRF, to pursue high fidelity view synthesis on the challenging scenarios of ultra high resolutions, building on…
Compressing Volumetric Radiance Fields to 1 MB
Lingzhi Li, Zhen Shen, Zhongshu Wang +2
Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast trai…
Streaming Radiance Fields for 3D Video Synthesis
Lingzhi Li, Zhen Shen, Zhongshu Wang +2
We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a singl…
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks
Haojin Yang, Zhen Shen, Yucheng Zhao
Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficul…