1 citations · 2 across the 16 of their papers we have counts for
17 papers
RefAdapt-DiT: Adaptive Joint Attention for Reference-Conditioned Diffusion Transformers
Jian Tang, Jiawei Fan, Qiannan Zhou +3
Diffusion Transformers (DiTs) have become the standard backbone for high-quality generative modeling, yet deploying them in conditional generation tasks remains computationally pro…
ForgeWM: Progressive Causal Training for Few-Step Action-Conditioned Video World Models
Xinye Li, Lingshuai Lin, Lei Wang +8
Action-conditioned video world models require low-latency causal generation and reliable responses to game-native controls. Although causal distillation enables one- or few-step vi…
Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation
Qi Chen, Shuhan Ding, Yu Gu +5
Variational autoencoders (VAEs) compress high resolution CT volumes into compact latents while preserving clinically relevant structure. However, training CT-specific VAEs from scr…
Beyond Pixel Histories: World Models with Persistent 3D State
Samuel Garcin, Thomas Walker, Steven McDonagh +5
Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities. However, existing models typically lack a 3D rep…
LIVE: Long-horizon Interactive Video World Modeling
Junchao Huang, Ziyang Ye, Xinting Hu +5
Autoregressive video world models predict future visual observations conditioned on actions. While effective over short horizons, these models often struggle with long-horizon gene…
Reinforcement Learning with Inverse Rewards for World Model Post-training
Yang Ye, Tianyu He, Shuo Yang +1
World models simulate dynamic environments, enabling agents to interact with diverse input modalities. Although recent advances have improved the visual quality and temporal consis…
Playing with Transformer at 30+ FPS via Next-Frame Diffusion
Xinle Cheng, Tianyu He, Jiayi Xu +3
Autoregressive video models offer distinct advantages over bidirectional diffusion models in creating interactive video content and supporting streaming applications with arbitrary…
Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Haoyu Wu, Diankun Wu, Tianyu He +4
Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture…
MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft
Junliang Guo, Yang Ye, Tianyu He +4
World modeling is a crucial task for enabling intelligent agents to effectively interact with humans and operate in dynamic environments. In this work, we propose MineWorld, a real…
Fast Autoregressive Video Generation with Diagonal Decoding
Yang Ye, Junliang Guo, Haoyu Wu +5
Autoregressive Transformer models have demonstrated impressive performance in video generation, but their sequential token-by-token decoding process poses a major bottleneck, parti…
AR4D: Autoregressive 4D Generation from Monocular Videos
Hanxin Zhu, Tianyu He, Xiqian Yu +3
Recent advancements in generative models have ignited substantial interest in dynamic 3D content creation (\ie, 4D generation). Existing approaches primarily rely on Score Distilla…
VidTok: A Versatile and Open-Source Video Tokenizer
Anni Tang, Tianyu He, Junliang Guo +3
Encoding video content into compact latent tokens has become a fundamental step in video generation and understanding, driven by the need to address the inherent redundancy in pixe…
VidTwin: Video VAE with Decoupled Structure and Dynamics
Yuchi Wang, Junliang Guo, Xinyi Xie +3
Recent advancements in video autoencoders (Video AEs) have significantly improved the quality and efficiency of video generation. In this paper, we propose a novel and compact vide…
IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI
Xiaoyu Chen, Junliang Guo, Tianyu He +5
We introduce Image-GOal Representations (IGOR), aiming to learn a unified, semantically consistent action space across human and various robots. Through this unified latent action…
Compositional 3D-aware Video Generation with LLM Director
Hanxin Zhu, Tianyu He, Anni Tang +3
Significant progress has been made in text-to-video generation through the use of powerful generative models and large-scale internet data. However, substantial challenges remain i…
Video In-context Learning: Autoregressive Transformers are Zero-Shot Video Imitators
Wentao Zhang, Junliang Guo, Tianyu He +3
People interact with the real-world largely dependent on visual signal, which are ubiquitous and illustrate detailed demonstrations. In this paper, we explore utilizing visual sign…
End-to-End Rate-Distortion Optimized 3D Gaussian Representation
Henan Wang, Hanxin Zhu, Tianyu He +4
3D Gaussian Splatting (3DGS) has become an emerging technique with remarkable potential in 3D representation and image rendering. However, the substantial storage overhead of 3DGS…