7 papers
Rays as Pixels: Learning A Joint Distribution of Videos and Camera Trajectories
Wonbong Jang, Shikun Liu, Soubhik Sanyal +6
Recovering camera parameters from images and rendering scenes from novel viewpoints have been treated as separate tasks in computer vision and graphics. This separation breaks down…
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…
EditCtrl: Disentangled Local and Global Control for Real-Time Generative Video Editing
Yehonathan Litman, Shikun Liu, Dario Seyb +5
High-fidelity generative video editing has seen significant quality improvements by leveraging pre-trained video foundation models. However, their computational cost is a major bot…
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…
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…
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…