Text-to-image generation models have achieved remarkable advancements in recent years, aiming to produce realistic images from textual descriptions. However, these models often struggle with generating anatomically accurate representations of human hands. The resulting images frequently exhibit issues such as incorrect numbers of fingers, unnatural twisting or interlacing of fingers, or blurred and indistinct hands. These issues stem from the inherent complexity of hand structures and the difficulty in aligning textual descriptions with precise visual depictions of hands. To address these challenges, we propose a novel approach named Hand1000 that enables the generation of realistic hand images with target gesture using only 1,000 training samples. The training of Hand1000 is divided into three stages with the first stage aiming to enhance the model’s understanding of hand anatomy by using a pre-trained hand gesture recognition model to extract gesture representation. The second stage further optimizes text embedding by incorporating the extracted hand gesture representation, to improve alignment between the textual descriptions and the generated hand images. The third stage utilizes the optimized embedding to fine-tune the Stable Diffusion model to generate realistic hand images. In addition, we construct the first publicly available dataset specifically designed for text-to-hand image generation. Based on the existing hand gesture recognition dataset, we adopt advanced image captioning models and LLaMA3 to generate high-quality textual descriptions enriched with detailed gesture information. Extensive experiments demonstrate that Hand1000 significantly outperforms existing models in producing anatomically correct hand images while faithfully representing other details in the text, such as faces, clothing and colors.

We aim to develop a model-based planning framework for world models that can be scaled with increasing model and data budgets for general-purpose manipulation tasks with only language and vision inputs. To this end, we present FLow-CentrIc generative Planning (FLIP), a model-based planning algorithm on visual space that features three key modules: 1) a multi-modal flow generation model as the general-purpose action proposal module; 2) a flow-conditioned video generation model as the dynamics module; and 3) a vision-language representation learning model as the value module. Given an initial image and language instruction as the goal, FLIP can progressively search for long-horizon flow and video plans that maximize the discounted return to accomplish the task. FLIP is able to synthesize long-horizon plans across objects, robots, and tasks with image flows as the general action representation, and the dense flow information also provides rich guidance for long-horizon video generation. In addition, the synthesized flow and video plans can guide the training of low-level control policies for robot execution. Experiments on diverse benchmarks demonstrate that FLIP can improve both the success rates and quality of long-horizon video plan synthesis and has the interactive world model property, opening up wider applications for future works. Video demos are on our website: https://nus-lins-lab.github.io/flipweb/.

Current Lifelong Person Re-Identification (LReID) methods focus on tackling a clean data stream with accurate labels. When noisy data with incorrect labels are given, their performance is severely degraded since the model inevitably and continually remembers erroneous knowledge induced by the label noises. Moreover, the well-known issue of catastrophic forgetting in LReID is exacerbated by noisy labels, which disrupt the retention of correct knowledge from previous models. Such a practical noisy LReID task is important but challenging, and rare works have attempted to handle it. In this paper, we initially investigate noisy LReID and propose a Continual Knowledge Purification (CKP) method to address the catastrophic remembering of erroneous knowledge and catastrophic forgetting of correct knowledge simultaneously. Specifically, a Cluster-aware Data Purification module (CDP) is designed to select clean labels based on clustering-guided label confidence estimation. Besides, an Iterative Label Rectification (ILR) pipeline is proposed to rectify wrong labels by fusing the prediction and label information throughout the training epochs. To handle the catastrophic remembering problem, an Erroneous Knowledge Filtering (EKF) algorithm is proposed to estimate and transfer the correct old knowledge to the new model. Finally, a Noisy LReID benchmark is constructed for performance evaluation and extensive experimental results demonstrate that our proposed CKP method achieves state-of-the-art performance. Our code is available at https://github.com/zhoujiahuan1991/MM2024-CKP

Most existing Re-ID studies focus on the short-term cloth-consistent setting and thus dominate by the visual appearance of clothing. However, the same person would wear different clothes and different people would wear the same clothes in reality, which invalidates these methods. To tackle the challenge of clothes change, we propose a Universal Clothing Attribute Disentanglement network (UCAD) which can effectively weaken the influence of clothing (identity-unrelated) and force the model to learn identity-related features that are unrelated to the worn clothing. For further study of Re-ID in cloth-changing scenarios, we construct a large-scale dataset called CSCC with the following unique features: (1) Severe: A large number of people have cloth-changing over four seasons. (2) High definition: The resolution of the cameras ranges from 1920×1080 to 3840×2160, which ensures that the recorded people are clear. Furthermore, we provide two variants of CSCC considering different degrees of cloth-changing, namely moderate and severe, so that researchers can effectively evaluate their models from various aspects. Experiments on several cloth-changing datasets including our CSCC and short-term dataset Market-1501 prove the superiority of UCAD. The dataset is available at https://github.com/yomin-y/UCAD.