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Zhekai Chen

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5 papers

2 author rows

ICLR Conference 2025 Conference Paper

Framer: Interactive Frame Interpolation

Wen Wang 0015
Qiuyu Wang
Kecheng Zheng
Hao Ouyang
Zhekai Chen
Biao Gong
Hao Chen 0041
Yujun Shen

We propose Framer for interactive frame interpolation, which targets producing smoothly transitioning frames between two images as per user creativity. Concretely, besides taking the start and end frames as inputs, our approach supports customizing the transition process by tailoring the trajectory of some selected keypoints. Such a design enjoys two clear benefits. First, incorporating human interaction mitigates the issue arising from numerous possibilities of transforming one image to another, and in turn enables finer control of local motions. Second, as the most basic form of interaction, keypoints help establish the correspondence across frames, enhancing the model to handle challenging cases (e.g., objects on the start and end frames are of different shapes and styles). It is noteworthy that our system also offers an "autopilot" mode, where we introduce a module to estimate the keypoints and refine the trajectory automatically, to simplify the usage in practice. Extensive experimental results demonstrate the appealing performance of Framer on various applications, such as image morphing, time-lapse video generation, cartoon interpolation, etc. The code, model, and interface are publicly accessible at https://github.com/aim-uofa/Framer.

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NeurIPS Conference 2025 Conference Paper

Speculative Jacobi-Denoising Decoding for Accelerating Autoregressive Text-to-image Generation

Yao Teng
Fu-Yun Wang
Xian Liu
Zhekai Chen
Han Shi
Yu Wang
Zhenguo Li
Weiyang Liu

As a new paradigm of visual content generation, autoregressive text-to-image models suffer from slow inference due to their sequential token-by-token decoding process, often requiring thousands of model forward passes to generate a single image. To address this inefficiency, we propose Speculative Jacobi-Denoising Decoding (SJD2), a framework that incorporates the denoising process into Jacobi iterations to enable parallel token generation in autoregressive models. Our method introduces a next-clean-token prediction paradigm that enables the pre-trained autoregressive models to accept noise-perturbed token embeddings and predict the next clean tokens through low-cost fine-tuning. This denoising paradigm guides the model towards more stable Jacobi trajectories. During inference, our method initializes token sequences with Gaussian noise and performs iterative next-clean-token-prediction in the embedding space. We employ a probabilistic criterion to verify and accept multiple tokens in parallel, and refine the unaccepted tokens for the next iteration with the denoising trajectory. Experiments show that our method can accelerate generation by reducing model forward passes while maintaining the visual quality of generated images.

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NeurIPS Conference 2025 Conference Paper

TTS-VAR: A Test-Time Scaling Framework for Visual Auto-Regressive Generation

Zhekai Chen
Ruihang Chu
Yukang Chen
Shiwei Zhang
Yujie Wei
Yingya Zhang
Xihui Liu

Scaling visual generation models is essential for real-world content creation, yet requires substantial training and computational expenses. Alternatively, test-time scaling has garnered growing attention due to resource efficiency and promising performance. In this work, we present the first general test-time scaling framework for visual auto-regressive (VAR) models, TTS-VAR, modeling the generation process as a path searching problem. Inspired by VAR's hierarchical coarse-to-fine multi-scale generation, our framework integrates two key components: (i) At coarse scales, we observe that generated tokens are hard for evaluation, possibly leading to erroneous acceptance of inferior samples or rejection of superior samples. Noticing that the coarse scales contain sufficient structural information, we propose clustering-based diversity search. It preserves structural variety through semantic feature clustering, enabling later selection on samples with higher potential. (ii) In fine scales, resampling-based potential selection prioritizes promising candidates using potential scores, which are defined as reward functions incorporating multi-scale generation history. To dynamically balance computational efficiency with exploration capacity, we further introduce an adaptive descending batch size schedule throughout the causal generation process. Experiments on the powerful VAR model Infinity2B show a notable 8. 7% GenEval score improvement (0. 69→0. 75). Key insights reveal that early-stage structural features effectively influence final quality, and resampling efficacy varies across generation scales.

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NeurIPS Conference 2025 Conference Paper

Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance

Ruihang Chu
Yefei He
Zhekai Chen
Shiwei Zhang
Xiaogang Xu
Bin Xia
Dingdong WANG
Hongwei Yi

We present Wan-Move, a simple and scalable framework that brings motion control to video generative models. Existing motion-controllable methods typically suffer from coarse control granularity and limited scalability, leaving their outputs insufficient for practical use. We narrow this gap by achieving precise and high-quality motion control. Our core idea is to directly make the original condition features motion-aware for guiding video synthesis. To this end, we first represent object motions with dense point trajectories, allowing fine-grained control over the scene. We then project these trajectories into latent space and propagate the first frame's features along each trajectory, producing an aligned spatiotemporal feature map that tells how each scene element should move. This feature map serves as the updated latent condition, which is naturally integrated into the off-the-shelf image-to-video model, e. g. , Wan-I2V-14B, as motion guidance without any architecture change. It removes the need for auxiliary motion encoders and makes fine-tuning base models easily scalable. Through scaled training, Wan-Move generates 5-second, 480p videos whose motion controllability rivals Kling 1. 5 Pro's commercial Motion Brush, as indicated by user studies. To support comprehensive evaluation, we further design MoveBench, a rigorously curated benchmark featuring diverse content categories and hybrid-verified annotations. It is distinguished by larger data volume, longer video durations, and high-quality motion annotations. Extensive experiments on MoveBench and the public dataset consistently show Wan-Move's superior motion quality. Code, models, and benchmark data are made available.

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NeurIPS Conference 2024 Conference Paper

Image Textualization: An Automatic Framework for Generating Rich and Detailed Image Descriptions

Renjie Pi
Jianshu Zhang
Jipeng Zhang
Rui Pan
Zhekai Chen
Tong Zhang

Image description datasets play a crucial role in the advancement of various applications such as image understanding, text-to-image generation, and text-image retrieval. Currently, image description datasets primarily originate from two sources. One source is the scraping of image-text pairs from the web. Despite their abundance, these descriptions are often of low quality and noisy. Another way is through human labeling. Datasets such as COCO are generally very short and lack details. Although detailed image descriptions can be annotated by humans, the high cost limits their quantity and feasibility. These limitations underscore the need for more efficient and scalable methods to generate accurate and detailed image descriptions. In this paper, we propose an innovative framework termed Image Textualization, which automatically produces high-quality image descriptions by leveraging existing mult-modal large language models (MLLMs) and multiple vision expert models in a collaborative manner. We conduct various experiments to validate the high quality of the descriptions constructed by our framework. Furthermore, we show that MLLMs fine-tuned on our dataset acquire an unprecedented capability of generating richer image descriptions, substantially increasing the length and detail of their output with even less hallucinations.

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