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Qiushan Guo

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5 papers

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NeurIPS Conference 2025 Conference Paper

OmniGen-AR: AutoRegressive Any-to-Image Generation

Junke Wang
Xun Wang
Qiushan Guo
Peize Sun
Weilin Huang
Zuxuan Wu
Yu-Gang Jiang

Autoregressive (AR) models have demonstrated strong potential in visual generation, offering competitive performance with simple architectures and optimization objectives. However, existing methods are typically limited to single-modality conditions, \eg, text or category labels, restricting their applicability in real-world scenarios that demand image synthesis from diverse forms of controls. In this work, we present \system, the first unified autoregressive framework for Any-to-Image generation. By discretizing various visual conditions through a shared visual tokenizer and text prompts with a text tokenizer, \system supports a broad spectrum of conditional inputs within a single model, including text (text-to-image generation), spatial signals (segmentation-to-image and depth-to-image), and visual context (image editing, frame prediction, and text-to-video generation). To mitigate the risk of information leakage from condition tokens to content tokens, we introduce Disentangled Causal Attention (DCA), which separates the full-sequence causal mask into condition causal attention and content causal attention. It serves as a training-time regularizer without affecting the standard next-token prediction during inference. With this design, \system achieves new state-of-the-art results across a range of benchmark, \eg, 0. 63 on GenEval and 80. 02 on VBench, demonstrating its effectiveness in flexible and high-fidelity visual generation.

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NeurIPS Conference 2025 Conference Paper

WorldWeaver: Generating Long-Horizon Video Worlds via Rich Perception

Zhiheng Liu
Xueqing Deng
Shoufa Chen
Angtian Wang
Qiushan Guo
Mingfei Han
Zeyue Xue
Mengzhao Chen

Generative video modeling has made significant strides, yet ensuring structural and temporal consistency over long sequences remains a challenge. Current methods predominantly rely on RGB signals, leading to accumulated errors in object structure and motion over extended durations. To address these issues, we introduce WorldWeaver, a robust framework for long video generation that jointly models RGB frames and perceptual conditions within a unified long-horizon modeling scheme. Our training framework offers three key advantages. First, by jointly predicting perceptual conditions and color information from a unified representation, it significantly enhances temporal consistency and motion dynamics. Second, by leveraging depth cues, which we observe to be more resistant to drift than RGB, we construct a memory bank that preserves clearer contextual information, improving quality in long-horizon video generation. Third, we employ segmented noise scheduling for training prediction groups, which further mitigates drift and reduces computational cost. Extensive experiments on both diffusion and rectified flow-based models demonstrate the effectiveness of WorldWeaver in reducing temporal drift and improving the fidelity of generated videos.

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NeurIPS Conference 2024 Conference Paper

SpatialRGPT: Grounded Spatial Reasoning in Vision-Language Models

An-Chieh Cheng
Hongxu Yin
Yang Fu
Qiushan Guo
Ruihan Yang
Jan Kautz
Xiaolong Wang
Sifei Liu

Vision Language Models (VLMs) have demonstrated remarkable performance in 2D vision and language tasks. However, their ability to reason about spatial arrangements remains limited. In this work, we introduce Spatial Region GPT (SpatialRGPT) to enhance VLMs’ spatial perception and reasoning capabilities. SpatialRGPT advances VLMs’ spatial understanding through two key innovations: (i) a data curation pipeline that enables effective learning of regional representation from 3D scene graphs, and (ii) a flexible ``plugin'' module for integrating depth information into the visual encoder of existing VLMs. During inference, when provided with user-specified region proposals, SpatialRGPT can accurately perceive their relative directions and distances. Additionally, we propose SpatialRGBT-Bench, a benchmark with ground-truth 3D annotations encompassing indoor, outdoor, and simulated environments, for evaluating 3D spatial cognition in Vision-Language Models (VLMs). Our results demonstrate that SpatialRGPT significantly enhances performance in spatial reasoning tasks, both with and without local region prompts. The model also exhibits strong generalization capabilities, effectively reasoning about complex spatial relations and functioning as a region-aware dense reward annotator for robotic tasks. Code, dataset, and benchmark are released at https: //www. anjiecheng. me/SpatialRGPT.

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NeurIPS Conference 2023 Conference Paper

RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths

Zeyue Xue
Guanglu Song
Qiushan Guo
Boxiao Liu
Zhuofan Zong
Yu Liu
Ping Luo

Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images, which accurately portray the text prompts, encompassing multiple nouns, adjectives, and verbs. This is achieved by stacking tens of mixture-of-experts (MoEs) layers, i. e. , space-MoE and time-MoE layers, enabling billions of diffusion paths (routes) from the network input to the output. Each path intuitively functions as a "painter" for depicting a particular textual concept onto a specified image region at a diffusion timestep. Comprehensive experiments reveal that RAPHAEL outperforms recent cutting-edge models, such as Stable Diffusion, ERNIE-ViLG 2. 0, DeepFloyd, and DALL-E 2, in terms of both image quality and aesthetic appeal. Firstly, RAPHAEL exhibits superior performance in switching images across diverse styles, such as Japanese comics, realism, cyberpunk, and ink illustration. Secondly, a single model with three billion parameters, trained on 1, 000 A100 GPUs for two months, achieves a state-of-the-art zero-shot FID score of 6. 61 on the COCO dataset. Furthermore, RAPHAEL significantly surpasses its counterparts in human evaluation on the ViLG-300 benchmark. We believe that RAPHAEL holds the potential to propel the frontiers of image generation research in both academia and industry, paving the way for future breakthroughs in this rapidly evolving field. More details can be found on a webpage: https: //raphael-painter. github. io/.

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NeurIPS Conference 2022 Conference Paper

Rethinking Resolution in the Context of Efficient Video Recognition

Chuofan Ma
Qiushan Guo
Yi Jiang
Ping Luo
Zehuan Yuan
Xiaojuan Qi

In this paper, we empirically study how to make the most of low-resolution frames for efficient video recognition. Existing methods mainly focus on developing compact networks or alleviating temporal redundancy of video inputs to increase efficiency, whereas compressing frame resolution has rarely been considered a promising solution. A major concern is the poor recognition accuracy on low-resolution frames. We thus start by analyzing the underlying causes of performance degradation on low-resolution frames. Our key finding is that the major cause of degradation is not information loss in the down-sampling process, but rather the mismatch between network architecture and input scale. Motivated by the success of knowledge distillation (KD), we propose to bridge the gap between network and input size via cross-resolution KD (ResKD). Our work shows that ResKD is a simple but effective method to boost recognition accuracy on low-resolution frames. Without bells and whistles, ResKD considerably surpasses all competitive methods in terms of efficiency and accuracy on four large-scale benchmark datasets, i. e. , ActivityNet, FCVID, Mini-Kinetics, Something-Something V2. In addition, we extensively demonstrate its effectiveness over state-of-the-art architectures, i. e. , 3D-CNNs and Video Transformers, and scalability towards super low-resolution frames. The results suggest ResKD can serve as a general inference acceleration method for state-of-the-art video recognition. Our code will be available at https: //github. com/CVMI-Lab/ResKD.

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