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20232026
most citedLearning Flow Fields in Attention for Controllable Person Image Generation

1 citations · 2 across the 9 of their papers we have counts for

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cs.CV2026

Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation

Zhiheng Liu, Weiming Ren, Xiaoke Huang +12

Unified multimodal models typically rely on pretrained vision encoders and use separate visual representations for understanding and generation, creating misalignment between the t…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024★ 1 cited

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…

cs.CV2023★ 1 cited

GenTron: Diffusion Transformers for Image and Video Generation

Shoufa Chen, Mengmeng Xu, Jiawei Ren +7

In this study, we explore Transformer-based diffusion models for image and video generation. Despite the dominance of Transformer architectures in various fields due to their flexi…