1 citations · 2 across the 14 of their papers we have counts for
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Native Action-Prior Learning from Videos for World Action Models
Zhaochong An, Fei Zhang, Menglin Jia +10
World action models integrate future visual dynamics with robot action prediction, but their scalability remains limited by the need for action-annotated robot trajectories. Observ…
In-Flight KV Cache with Clean Anchors for Faster Autoregressive Video Diffusion
Yikai Wang, Xiao Han, Mengmeng Xu +9
Few-step autoregressive video diffusion generates a long video by splitting the video into temporal chunks and generating chunk-by-chunk, each through a short sequence of denoising…
Are Text-to-Image Models Inductivist Turkeys? A Counterfactual Benchmark for Causal Reasoning
Jiayi Lei, Yuandong Pu, Xingyu Han +8
Text-to-image (T2I) generation models have achieved remarkable progress in producing visually realistic images from natural language prompts. Yet it remains unclear whether their s…
OrbitNVS: Harnessing Video Diffusion Priors for Novel View Synthesis
Jinglin Liang, Zijian Zhou, Rui Huang +2
Novel View Synthesis (NVS) aims to generate unseen views of a 3D object given a limited number of known views. Existing methods often struggle to synthesize plausible views for uno…
TransText: Alpha-as-RGB Representation for Transparent Text Animation
Fei Zhang, Zijian Zhou, Bohao Tang +9
We introduce the first method, to the best of our knowledge, for adapting image-to-video models to layer-aware text (glyph) animation, a capability critical for practical dynamic v…
HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming
Haonan Qiu, Shikun Liu, Zijian Zhou +10
High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical infere…
OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory
Zhaochong An, Menglin Jia, Haonan Qiu +12
Storytelling in real-world videos often unfolds through multiple shots -- discontinuous yet semantically connected clips that together convey a coherent narrative. However, existin…
Scaling Zero-Shot Reference-to-Video Generation
Zijian Zhou, Shikun Liu, Haozhe Liu +14
Reference-to-video (R2V) generation aims to synthesize videos that align with a text prompt while preserving the subject identity from reference images. However, current R2V method…
TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models
Zhiheng Liu, Weiming Ren, Haozhe Liu +22
Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified conti…
Mixture of States: Routing Token-Level Dynamics for Multimodal Generation
Haozhe Liu, Ding Liu, Mingchen Zhuge +16
We introduce MoS (Mixture of States), a novel fusion paradigm for multimodal diffusion models that merges modalities using flexible, state-based interactions. The core of MoS is a…
Scaling Sequence-to-Sequence Generative Neural Rendering
Shikun Liu, Kam Woh Ng, Wonbong Jang +9
We present Kaleido, a family of generative models designed for photorealistic, unified object- and scene-level neural rendering. Kaleido operates on the principle that 3D can be re…
Learning Flow Fields in Attention for Controllable Person Image Generation
Zijian Zhou, Shikun Liu, Xiao Han +11
Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person's appearance or pose. However, prior…
MarDini: Masked Autoregressive Diffusion for Video Generation at Scale
Haozhe Liu, Shikun Liu, Zijian Zhou +12
We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework. Here, MAR…