4 citations · 6 across the 18 of their papers we have counts for
21 papers
UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models
Xidong Zhang, Yichi Zhang, Jiaxin Shi +5
Vision-language-action (VLA) models have achieved strong performance in many robotic manipulation tasks, yet remain limited in contact-rich dexterous manipulation. To overcome this…
Variational Learning for Insertion-based Generation
Yangtian Zhang, Zhe Wang, Arthur Gretton +4
Non-monotonic sequence generation methods, such as masked diffusion models, provide a flexible alternative to left-to-right autoregressive modeling by allowing tokens to be generat…
3DThinkVLA: Endowing Vision-Language-Action Models with Latent 3D Priors via 3D-Thinking-Guided Co-training
Jiaxin Shi, Xidong Zhang, Fucai Zhu +3
We propose a 3D-thinking-guided co-training framework that enables vision-language-action (VLA) models to perform 3D spatial reasoning implicitly during action prediction. Our core…
RealCam: Real-Time Novel-View Video Generation with Interactive Camera Control
Youcan Xu, Jiaxin Shi, Zhen Wang +5
Camera-controlled video-to-video (V2V) generation enables dynamic viewpoint synthesis from monocular footage, holding immense potential for interactive filmmaking and live broadcas…
DiT as Real-Time Rerenderer: Streaming Video Stylization with Autoregressive Diffusion Transformer
Hengye Lyu, Zisu Li, Yue Hong +4
Recent advances in video generation models has significantly accelerated video generation and related downstream tasks. Among these, video stylization holds important research valu…
Generative Frontiers: Why Evaluation Matters for Diffusion Language Models
Patrick Pynadath, Jiaxin Shi, Ruqi Zhang
Diffusion language models have seen exciting recent progress, offering far more flexibility in generative trajectories than autoregressive models. This flexibility has motivated a…
SpriteHand: Real-Time Versatile Hand-Object Interaction with Autoregressive Video Generation
Zisu Li, Hengye Lyu, Jiaxin Shi +4
Modeling and synthesizing complex hand-object interactions remains a significant challenge, even for state-of-the-art physics engines. Conventional simulation-based approaches rely…
Generative Augmented Reality: Paradigms, Technologies, and Future Applications
Chen Liang, Jiawen Zheng, Yufeng Zeng +7
This paper introduces Generative Augmented Reality (GAR) as a next-generation paradigm that reframes augmentation as a process of world re-synthesis rather than world composition b…
CoMo: Compositional Motion Customization for Text-to-Video Generation
Youcan Xu, Zhen Wang, Jiaxin Shi +6
While recent text-to-video models excel at generating diverse scenes, they struggle with precise motion control, particularly for complex, multi-subject motions. Although methods f…
CANDI: Hybrid Discrete-Continuous Diffusion Models
Patrick Pynadath, Jiaxin Shi, Ruqi Zhang
While continuous diffusion has shown remarkable success in continuous domains such as image generation, its direct application to discrete data has underperformed pure discrete for…
Real-Time Motion-Controllable Autoregressive Video Diffusion
Kesen Zhao, Jiaxin Shi, Beier Zhu +5
Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) appro…
LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair
Xue Song, Jiequan Cui, Hanwang Zhang +4
In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient s…
Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing
Kaifeng Gao, Jiaxin Shi, Hanwang Zhang +3
With the advance of diffusion models, today's video generation has achieved impressive quality. To extend the generation length and facilitate real-world applications, a majority o…
TOP:A New Target-Audience Oriented Content Paraphrase Task
Boda Lin, Jiaxin Shi, Haolong Yan +3
Recommendation systems usually recommend the existing contents to different users. However, in comparison to static recommendation methods, a recommendation logic that dynamically…
Informed Correctors for Discrete Diffusion Models
Yixiu Zhao, Jiaxin Shi, Feng Chen +3
Discrete diffusion has emerged as a powerful framework for generative modeling in discrete domains, yet efficiently sampling from these models remains challenging. Existing samplin…
ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models
Kaifeng Gao, Jiaxin Shi, Hanwang Zhang +2
With the advance of diffusion models, today's video generation has achieved impressive quality. But generating temporal consistent long videos is still challenging. A majority of v…
Simplified and Generalized Masked Diffusion for Discrete Data
Jiaxin Shi, Kehang Han, Zhe Wang +2
Masked (or absorbing) diffusion is actively explored as an alternative to autoregressive models for generative modeling of discrete data. However, existing work in this area has be…
Cross-Lingual Transfer for Natural Language Inference via Multilingual Prompt Translator
Xiaoyu Qiu, Yuechen Wang, Jiaxin Shi +2
Based on multilingual pre-trained models, cross-lingual transfer with prompt learning has shown promising effectiveness, where soft prompt learned in a source language is transferr…
Space Narrative: Generating Images and 3D Scenes of Chinese Garden from Text using Deep Learning
Jiaxi Shi1, Hao Hua1
The consistent mapping from poems to paintings is essential for the research and restoration of traditional Chinese gardens. But the lack of firsthand ma-terial is a great challeng…
Sequence Modeling with Multiresolution Convolutional Memory
Jiaxin Shi, Ke Alexander Wang, Emily B. Fox
Efficiently capturing the long-range patterns in sequential data sources salient to a given task -- such as classification and generative modeling -- poses a fundamental challenge.…
Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
Ke Alexander Wang, Matthew E. Levine, Jiaxin Shi +1
Traditional models of glucose-insulin dynamics rely on heuristic parameterizations chosen to fit observations within a laboratory setting. However, these models cannot describe glu…