CVPR 2024 论文列表

Reconstructing 3D hand mesh robustly from a single image is very challenging due to the lack of diversity in existing real-world datasets. While data synthesis helps relieve the issue the syn-to-real gap still hinders its usage. In this work we present HandBooster a new approach to uplift the data diversity and boost the 3D hand-mesh reconstruction performance by training a conditional generative space on hand-object interactions and purposely sampling the space to synthesize effective data samples. First we construct versatile content-aware conditions to guide a diffusion model to produce realistic images with diverse hand appearances poses views and backgrounds; favorably accurate 3D annotations are obtained for free. Then we design a novel condition creator based on our similarity-aware distribution sampling strategies to deliberately find novel and realistic interaction poses that are distinctive from the training set. Equipped with our method several baselines can be significantly improved beyond the SOTA on the HO3D and DexYCB benchmarks. Our code will be released on https://github.com/hxwork/HandBooster_Pytorch.

Text-to-image generative models can generate high-quality humans but realism is lost when generating hands. Common artifacts include irregular hand poses shapes incorrect numbers of fingers and physically implausible finger orientations. To generate images with realistic hands we propose a novel diffusion-based architecture called HanDiffuser that achieves realism by injecting hand embeddings in the generative process. HanDiffuser consists of two components: a Text-to-Hand-Params diffusion model to generate SMPL-Body and MANO-Hand parameters from input text prompts and a Text-Guided Hand-Params-to-Image diffusion model to synthesize images by conditioning on the prompts and hand parameters generated by the previous component. We incorporate multiple aspects of hand representation including 3D shapes and joint-level finger positions orientations and articulations for robust learning and reliable performance during inference. We conduct extensive quantitative and qualitative experiments and perform user studies to demonstrate the efficacy of our method in generating images with high-quality hands.

We introduce "HallusionBench" a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs) such as GPT-4V(ision) Gemini Pro Vision Claude 3 and LLaVA-1.5 by emphasizing nuanced understanding and interpretation of visual data. The benchmark comprises 346 images paired with 1129 questions all meticulously crafted by human experts. We introduce a novel structure for these visual questions designed to establish control groups. This structure enables us to conduct a quantitative analysis of the models' response tendencies logical consistency and various failure modes. In our evaluation on HallusionBench we benchmarked 15 different models highlighting a 31.42% question-pair accuracy achieved by the state-of-the-art GPT-4V. Notably all other evaluated models achieve accuracy below 16%. Moreover our analysis not only highlights the observed failure modes including language hallucination and visual illusion but also deepens an under standing of these pitfalls. Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs. Based on these insights we suggest potential pathways for their future improvement. The benchmark and codebase can be accessed at https://github.com/tianyilab/HallusionBench.

Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However MLLMs still face a fundamental limitation of hallucinations where they tend to generate erroneous or fabricated information. In this paper we address hallucinations in MLLMs from a novel perspective of representation learning. We first analyzed the representation distribution of textual and visual tokens in MLLM revealing two important findings: 1) there is a significant gap between textual and visual representations indicating unsatisfactory cross-modal representation alignment; 2) representations of texts that contain and do not contain hallucinations are entangled making it challenging to distinguish them. These two observations inspire us with a simple yet effective method to mitigate hallucinations. Specifically we introduce contrastive learning into MLLMs and use text with hallucination as hard negative examples naturally bringing representations of non-hallucinative text and visual samples closer while pushing way representations of non-hallucinating and hallucinative text. We evaluate our method quantitatively and qualitatively showing its effectiveness in reducing hallucination occurrences and improving performance across multiple benchmarks. On the MMhal-Bench benchmark our method obtains a 34.66% /29.5% improvement over the baseline MiniGPT-4/LLaVA. Our code is available on https://github.com/X-PLUG/mPLUG-HalOwl/tree/main/hacl.

Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multimodal understanding and generation tasks. However the hallucinations inherent in machine-generated data which could lead to hallucinatory outputs in MLLMs remain under-explored. This work aims to investigate various hallucinations (i.e. object relation attribute hallucinations) and mitigate those hallucinatory toxicities in large-scale machine-generated visual instruction datasets. Drawing on the human ability to identify factual errors we present a novel hallucination detection and elimination framework HalluciDoctor based on the cross-checking paradigm. We use our framework to identify and eliminate hallucinations in the training data automatically. Interestingly HalluciDoctor also indicates that spurious correlations arising from long-tail object co-occurrences contribute to hallucinations. Based on that we execute counterfactual visual instruction expansion to balance data distribution thereby enhancing MLLMs' resistance to hallucinations. Comprehensive experiments on hallucination evaluation benchmarks show that our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA. The data and code for this paper are publicly available.

We contribute the Habitat Synthetic Scene Dataset a dataset of 211 high-quality 3D scenes and use it to test navigation agent generalization to realistic 3D environments. Our dataset represents real interiors and contains a diverse set of 18656 models of real-world objects. We investigate the impact of synthetic 3D scene dataset scale and realism on the task of training embodied agents to find and navigate to objects (ObjectGoal navigation). By comparing to synthetic 3D scene datasets from prior work we find that scale helps in generalization but the benefits quickly saturate making visual fidelity and correlation to real-world scenes more important. Our experiments show that agents trained on our smaller-scale dataset can match or outperform agents trained on much larger datasets. Surprisingly we observe that agents trained on just 122 scenes from our dataset outperform agents trained on 10000 scenes from the ProcTHOR-10K dataset in terms of zero-shot generalization in real-world scanned environments.

Human-centric 3D scene understanding has recently drawn increasing attention driven by its critical impact on robotics. However human-centric real-life scenarios are extremely diverse and complicated and humans have intricate motions and interactions. With limited labeled data supervised methods are difficult to generalize to general scenarios hindering real-life applications. Mimicking human intelligence we propose an unsupervised 3D detection method for human-centric scenarios by transferring the knowledge from synthetic human instances to real scenes. To bridge the gap between the distinct data representations and feature distributions of synthetic models and real point clouds we introduce novel modules for effective instance-to-scene representation transfer and synthetic-to-real feature alignment. Remarkably our method exhibits superior performance compared to current state-of-the-art techniques achieving 87.8% improvement in mAP and closely approaching the performance of fully supervised methods (62.15 mAP vs. 69.02 mAP) on HuCenLife Dataset.

Recent advances in neural rendering have improved both training and rendering times by orders of magnitude. While these methods demonstrate state-of-the-art quality and speed they are designed for photogrammetry of static scenes and do not generalize well to freely moving humans in the environment. In this work we introduce Human Gaussian Splats (HUGS) that represents an animatable human together with the scene using 3D Gaussian Splatting (3DGS). Our method takes only a monocular video with a small number of (50-100) frames and it automatically learns to disentangle the static scene and a fully animatable human avatar within 30 minutes. We utilize the SMPL body model to initialize the human Gaussians. To capture details that are not modeled by SMPL (e.g cloth hairs) we allow the 3D Gaussians to deviate from the human body model. Utilizing 3D Gaussians for animated humans brings new challenges including the artifacts created when articulating the Gaussians. We propose to jointly optimize the linear blend skinning weights to coordinate the movements of individual Gaussians during animation. Our approach enables novel-pose synthesis of human and novel view synthesis of both the human and the scene. We achieve state-of-the-art rendering quality with a rendering speed of 60 FPS while being 100x faster to train over previous work.

Holistic understanding of urban scenes based on RGB images is a challenging yet important problem. It encompasses understanding both the geometry and appearance to enable novel view synthesis parsing semantic labels and tracking moving objects. Despite considerable progress existing approaches often focus on specific aspects of this task and require additional inputs such as LiDAR scans or manually annotated 3D bounding boxes. In this paper we introduce a novel pipeline that utilizes 3D Gaussian Splatting for holistic urban scene understanding. Our main idea involves the joint optimization of geometry appearance semantics and motion using a combination of static and dynamic 3D Gaussians where moving object poses are regularized via physical constraints. Our approach offers the ability to render new viewpoints in real-time yielding 2D and 3D semantic information with high accuracy and reconstruct dynamic scenes even in scenarios where 3D bounding box detection are highly noisy. Experimental results on KITTI KITTI-360 and Virtual KITTI 2 demonstrate the effectiveness of our approach. Our project page is at https://xdimlab.github.io/hugs_website.

Leveraging vast training data multimodal large language models (MLLMs) have demonstrated formidable general visual comprehension capabilities and achieved remarkable performance across various tasks. However their performance in visual document understanding still leaves much room for improvement. This discrepancy is primarily attributed to the fact that visual document understanding is a fine-grained prediction task. In natural scenes MLLMs typically use low-resolution images leading to a substantial loss of visual information. Furthermore general-purpose MLLMs do not excel in handling document-oriented instructions. In this paper we propose a High-Resolution Visual Document Assistant (HRVDA) which bridges the gap between MLLMs and visual document understanding. This model employs a content filtering mechanism and an instruction filtering module to separately filter out the content-agnostic visual tokens and instruction-agnostic visual tokens thereby achieving efficient model training and inference for high-resolution images. In addition we construct a document-oriented visual instruction tuning dataset and apply a multi-stage training strategy to enhance the model's document modeling capabilities. Extensive experiments demonstrate that our model achieves state-of-the-art performance across multiple document understanding datasets while maintaining training efficiency and inference speed comparable to low-resolution models.

Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm which predicts the future trajectory by using a fixed duration of historical frames. These methods make the predictions independently even at adjacent time steps which leads to potential instability and temporal inconsistency. As successive time steps have largely overlapping historical frames their forecasting should have intrinsic correlation such as overlapping predicted trajectories should be consistent or be different but share the same motion goal depending on the road situation. Motivated by this in this work we introduce HPNet a novel dynamic trajectory forecasting method. Aiming for stable and accurate trajectory forecasting our method leverages not only historical frames including maps and agent states but also historical predictions. Specifically we newly design a Historical Prediction Attention module to automatically encode the dynamic relationship between successive predictions. Besides it also extends the attention range beyond the currently visible window benefitting from the use of historical predictions. The proposed Historical Prediction Attention together with the Agent Attention and Mode Attention is further formulated as the Triple Factorized Attention module serving as the core design of HPNet. Experiments on the Argoverse and INTERACTION datasets show that HPNet achieves state-of-the-art performance and generates accurate and stable future trajectories. Our code are available at https://github.com/XiaolongTang23/HPNet.

Event-based semantic segmentation has gained popularity due to its capability to deal with scenarios under high-speed motion and extreme lighting conditions which cannot be addressed by conventional RGB cameras. Since it is hard to annotate event data previous approaches rely on event-to-image reconstruction to obtain pseudo labels for training. However this will inevitably introduce noise and learning from noisy pseudo labels especially when generated from a single source may reinforce the errors. This drawback is also called confirmation bias in pseudo-labeling. In this paper we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation HPL-ESS to alleviate the influence of noisy pseudo labels. In particular we first employ a plain unsupervised domain adaptation framework as our baseline which can generate a set of pseudo labels through self-training. Then we incorporate offline event-to-image reconstruction into the framework and obtain another set of pseudo labels by predicting segmentation maps on the reconstructed images. A noisy label learning strategy is designed to mix the two sets of pseudo labels and enhance the quality. Moreover we propose a soft prototypical alignment module to further improve the consistency of target domain features. Extensive experiments show that our proposed method outperforms existing state-of-the-art methods by a large margin on the DSEC-Semantic dataset (+5.88% accuracy +10.32% mIoU) which even surpasses several supervised methods.

Since humans interact with diverse objects every day the holistic 3D capture of these interactions is important to understand and model human behaviour. However most existing methods for hand-object reconstruction from RGB either assume pre-scanned object templates or heavily rely on limited 3D hand-object data restricting their ability to scale and generalize to more unconstrained interaction settings. To address this we introduce HOLD -- the first category-agnostic method that reconstructs an articulated hand and an object jointly from a monocular interaction video. We develop a compositional articulated implicit model that can reconstruct disentangled 3D hands and objects from 2D images. We also further incorporate hand-object constraints to improve hand-object poses and consequently the reconstruction quality. Our method does not rely on any 3D hand-object annotations while significantly outperforming fully-supervised baselines in both in-the-lab and challenging in-the-wild settings. Moreover we qualitatively show its robustness in reconstructing from in-the-wild videos. See https://github.com/zc-alexfan/hold for code data models and updates.

We address the challenging task of identifying segmenting and tracking hand-held objects which is crucial for applications such as human action segmentation and performance evaluation. This task is particularly challenging due to heavy occlusion rapid motion and the transitory nature of objects being hand-held where an object may be held released and subsequently picked up again. To tackle these challenges we have developed a novel transformer-based architecture called HOIST-Former. HOIST-Former is adept at spatially and temporally segmenting hands and objects by iteratively pooling features from each other ensuring that the processes of identification segmentation and tracking of hand-held objects depend on the hands' positions and their contextual appearance. We further refine HOIST-Former with a contact loss that focuses on areas where hands are in contact with objects. Moreover we also contribute an in-the-wild video dataset called HOIST which comprises 4125 videos complete with bounding boxes segmentation masks and tracking IDs for hand-held objects. Through experiments on the HOIST dataset and two additional public datasets we demonstrate the efficacy of HOIST-Former in segmenting and tracking hand-held objects.

Human hands are highly articulated and versatile at handling objects. Jointly estimating the 3D poses of a hand and the object it manipulates from a monocular camera is challenging due to frequent occlusions. Thus existing methods often rely on intermediate 3D shape representations to increase performance. These representations are typically explicit such as 3D point clouds or meshes and thus provide information in the direct surroundings of the intermediate hand pose estimate. To address this we introduce HOISDF a Signed Distance Field (SDF) guided hand-object pose estimation network which jointly exploits hand and object SDFs to provide a global implicit representation over the complete reconstruction volume. Specifically the role of the SDFs is threefold: equip the visual encoder with implicit shape information help to encode hand-object interactions and guide the hand and object pose regression via SDF-based sampling and by augmenting the feature representations. We show that HOISDF achieves state-of-the-art results on hand-object pose estimation benchmarks (DexYCB and HO3Dv2). Code is available https://github.com/amathislab/HOISDF.

3D hand-object interaction data is scarce due to the hardware constraints in scaling up the data collection process. In this paper we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data. Our model is a conditional diffusion model that takes both the 3D hand-object geometric structure and text description as inputs for image synthesis. This offers a more controllable and realistic synthesis as we can specify the structure and style inputs in a disentangled manner. HOIDiffusion is trained by leveraging a diffusion model pre-trained on large-scale natural images and a few 3D human demonstrations. Beyond controllable image synthesis we adopt the generated 3D data for learning 6D object pose estimation and show its effectiveness in improving perception systems. Project page: https://mq-zhang1.github.io/HOIDiffusion.

To date the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research. The underlying challenge demands both a discriminating interpretation of language and a comprehensive physics-centric model supporting real-world dynamics. To ameliorate this paper advocates HOIAnimator a novel and interactive diffusion model with perception ability and also ingeniously crafted to revolutionize the animation of complex interactions from linguistic narratives. The effectiveness of our model is anchored in two ground-breaking innovations: (1) Our Perceptive Diffusion Models (PDM) brings together two types of models: one focused on human movements and the other on objects. This combination allows for animations where humans and objects move in concert with each other making the overall motion more realistic. Additionally we propose a Perceptive Message Passing (PMP) mechanism to enhance the communication bridging the two models ensuring that the animations are smooth and unified; (2) We devise an Interaction Contact Field (ICF) a sophisticated model that implicitly captures the essence of HOIs. Beyond mere predictive contact points the ICF assesses the proximity of human and object to their respective environment informed by a probabilistic distribution of interactions learned throughout the denoising phase. Our comprehensive evaluation showcases HOIanimator's superior ability to produce dynamic context-aware animations that surpass existing benchmarks in text-driven animation synthesis.

Humans naturally interact with both others and the surrounding multiple objects engaging in various social activities. However recent advances in modeling human-object interactions mostly focus on perceiving isolated individuals and objects due to fundamental data scarcity. In this paper we introduce HOI-M^3 a novel large-scale dataset for modeling the interactions of Multiple huMans and Multiple objects. Notably it provides accurate 3D tracking for both humans and objects from dense RGB and object-mounted IMU inputs covering 199 sequences and 181M frames of diverse humans and objects under rich activities. With the unique HOI-M^3 dataset we introduce two novel data-driven tasks with companion strong baselines: monocular capture and unstructured generation of multiple human-object interactions. Extensive experiments demonstrate that our dataset is challenging and worthy of further research about multiple human-object interactions and behavior analysis. Our HOI-M^3 dataset corresponding codes and pre-trained models will be disseminated to the community for future research.

It is especially challenging to achieve real-time human motion tracking on a standalone VR Head-Mounted Display (HMD) such as Meta Quest and PICO. In this paper we propose HMD-Poser the first unified approach to recover full-body motions using scalable sparse observations from HMD and body-worn IMUs. In particular it can support a variety of input scenarios such as HMD HMD+2IMUs HMD+3IMUs etc. The scalability of inputs may accommodate users' choices for both high tracking accuracy and easy-to-wear. A lightweight temporal-spatial feature learning network is proposed in HMD-Poser to guarantee that the model runs in real-time on HMDs. Furthermore HMD-Poser presents online body shape estimation to improve the position accuracy of body joints. Extensive experimental results on the challenging AMASS dataset show that HMD-Poser achieves new state-of-the-art results in both accuracy and real-time performance. We also build a new free-dancing motion dataset to evaluate HMD-Poser's on-device performance and investigate the performance gap between synthetic data and real-captured sensor data. Finally we demonstrate our HMD-Poser with a real-time Avatar-driving application on a commercial HMD. Our code and free-dancing motion dataset are available \href https://pico-ai-team.github.io/hmd-poser here .