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Tianjiao Yu

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#artificial intelligence Preprint Oct 2026

CoDA: Co-Evolution of Data Engines and Action Policies

Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environments that address a policy's failures and supervision that helps it overcome them. We introduce CoDA, a framework in which a simulation data...

Tianjiao Yu, Zhi-Lin Ou, Yi-Fan Shen et al. · 0 citations
#artificial intelligence Preprint Oct 2026

SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation

High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize local geometry. While effective, this design fragments continuous surfaces into many local tokens, inflates generation cost, and often weakens topological consistency for...

Tianjiao Yu, Xin-Zhuo Li, Yi-Fan Shen et al. · 0 citations
Jul 2026

GraphVid: Interactive Graph-Controllable Video Generation

GraphVid is introduced, a graph-conditioned image-to-video generation model that enables interactive control through structured interaction graphs that delivers strong controllability and video quality and highlights the potential of structured semantic interfaces as a powerful paradigm for controllable video generatio...

Vedant r Shah, O. Susladkar, Tushar Prakash et al. · 0 citations
#artificial intelligence Preprint Sep 2026

One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing

Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We introduce EditVid, a training-free framework combining sparse causal memory for local coherence, correspondence-based post-attention token inj...

A. Juvekar, O. Susladkar, K. A. Nguyen et al. · 0 citations
Preprint Aug 2026

ChronoVision: Temporal Reasoning via Latent State Reconstruction

This work proposes ChronoVision, a multimodal framework designed to align visual logic with latent imagery, and introduces Vbvr-VQA, a novel dataset that evaluates temporal tracking by reformulating video reasoning into a strict image-ordering task.

Yi-Fan Shen, Jian Xu, Boyi Li et al. · 1 citation

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