CVPR 2024 Papers

Generative AI has made significant strides in computer vision particularly in text-driven image/video synthesis (T2I/T2V). Despite the notable advancements it remains challenging in human-centric content synthesis such as realistic dance generation. Current methodologies primarily tailored for human motion transfer encounter difficulties when confronted with real-world dance scenarios (e.g. social media dance) which require to generalize across a wide spectrum of poses and intricate human details. In this paper we depart from the traditional paradigm of human motion transfer and emphasize two additional critical attributes for the synthesis of human dance content in social media contexts: (i) Generalizability: the model should be able to generalize beyond generic human viewpoints as well as unseen human subjects backgrounds and poses; (ii) Compositionality: it should allow for the seamless composition of seen/unseen subjects backgrounds and poses from different sources. To address these challenges we introduce DISCO which includes a novel model architecture with disentangled control to improve the compositionality of dance synthesis and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DISCO can generate high-quality human dance images and videos with diverse appearances and flexible motions. Code is available at https://disco-dance.github.io/.

Motion capture from a limited number of body-worn sensors such as inertial measurement units (IMUs) and pressure insoles has important applications in health human performance and entertainment. Recent work has focused on accurately reconstructing whole-body motion from a specific sensor configuration using six IMUs. While a common goal across applications is to use the minimal number of sensors to achieve required accuracy the optimal arrangement of the sensors might differ from application to application. We propose a single diffusion model DiffusionPoser which reconstructs human motion in real-time from an arbitrary combination of sensors including IMUs placed at specified locations and pressure insoles. Unlike existing methods our model grants users the flexibility to determine the number and arrangement of sensors tailored to the specific activity of interest without the need for retraining. A novel autoregressive inferencing scheme ensures real-time motion reconstruction that closely aligns with measured sensor signals. The generative nature of DiffusionPoser ensures realistic behavior even for degrees-of-freedom not directly measured. Qualitative results can be found on our project website.

Reconstructing a clothed human from a single-view image has several challenging issues including flexibly representing various body shapes and poses estimating complete 3D geometry and consistent texture and achieving more fine-grained details. To address them we propose a new diffusion-based Fourier occupancy field method to improve the human representing ability and the geometry generating ability. First we estimate the back-view image from the given reference image by incorporating a style consistency constraint. Then we extract multi-scale features of the two images as conditional and design a diffusion model to generate the Fourier occupancy field in the wavelet domain. We refine the initial estimated Fourier occupancy field with image features as conditions to improve the geometric accuracy. Finally the reference and estimated back-view images are mapped onto the human model creating a textured clothed human model. Substantial experiments are conducted and the experimental results show that our method outperforms the state-of-the-art methods in geometry and texture reconstruction performance.

The recovery of occluded human meshes poses challenges for current methods due to the difficulty in extracting effective image features under severe occlusion. In this paper we introduce DPMesh an innovative framework for occluded human mesh recovery that capitalizes on the profound knowledge about object structure and spatial relationships embedded in a pre-trained text-to-image diffusion model. Unlike previous methods reliant on conventional backbones for vanilla feature extraction DPMesh seamlessly integrates the pre-trained denoising U-Net with potent priors as its image backbone and performs a single-step inference to provide occlusion-aware information. To enhance the perception capability for occluded poses DPMesh incorporates judicious guidance via condition injection which produces effective controls from 2D observations for the denoising U-Net. Furthermore we explore a dedicated noisy key-point reasoning approach to mitigate disturbances arising from occlusion and crowded scenarios. This strategy fully unleashes the perceptual capability of the diffusion prior thereby enhancing accuracy. Extensive quantitative and qualitative experiments affirm the efficacy of our framework as we outperform state-of-the-art methods on both occlusion-specific and standard datasets underscoring its ability to achieve precise and robust 3D human mesh recovery particularly in challenging scenarios involving occlusion and crowded scenes. Code is available at https://github.com/EternalEvan/DPMesh.

Listening head generation aims to synthesize a non-verbal responsive listener head by modeling the correlation between the speaker and the listener in dynamic conversion. The applications of listener agent generation in virtual interaction have promoted many works achieving diverse and fine-grained motion generation. However they can only manipulate motions through simple emotional labels but cannot freely control the listener's motions. Since listener agents should have human-like attributes (e.g. identity personality) which can be freely customized by users this limits their realism. In this paper we propose a user-friendly framework called CustomListener to realize the free-form text prior guided listener generation. To achieve speaker-listener coordination we design a Static to Dynamic Portrait module (SDP) which interacts with speaker information to transform static text into dynamic portrait token with completion rhythm and amplitude information. To achieve coherence between segments we design a Past Guided Generation module (PGG) to maintain the consistency of customized listener attributes through the motion prior and utilize a diffusion-based structure conditioned on the portrait token and the motion prior to realize the controllable generation. To train and evaluate our model we have constructed two text-annotated listening head datasets based on ViCo and RealTalk which provide text-video paired labels. Extensive experiments have verified the effectiveness of our model.

Human perception and understanding is a major domain of computer vision which like many other vision subdomains recently stands to gain from the use of large models pre-trained on large datasets. We hypothesize that the most common pre-training strategy of relying on general purpose object-centric image datasets such as ImageNet is limited by an important domain shift. On the other hand collecting domain-specific ground truth such as 2D or 3D labels does not scale well. Therefore we propose a pre-training approach based on self-supervised learning that works on human-centric data using only images. Our method uses pairs of images of humans: the first is partially masked and the model is trained to reconstruct the masked parts given the visible ones and a second image. It relies on both stereoscopic (cross-view) pairs and temporal (cross-pose) pairs taken from videos in order to learn priors about 3D as well as human motion. We pre-train a model for body-centric tasks and one for hand-centric tasks. With a generic transformer architecture these models outperform existing self-supervised pre-training methods on a wide set of human-centric downstream tasks and obtain state-of-the-art performance for instance when fine-tuning for model-based and model-free human mesh recovery.

In this work we propose a method to address the challenge of rendering a 3D human from a single image in a free-view manner. Some existing approaches could achieve this by using generalizable pixel-aligned implicit fields to reconstruct a textured mesh of a human or by employing a 2D diffusion model as guidance with the Score Distillation Sampling (SDS) method to lift the 2D image into 3D space. However a generalizable implicit field often results in an over-smooth texture field while the SDS method tends to lead to a texture-inconsistent novel view with the input image. In this paper we introduce a texture-consistent back view synthesis method that could transfer the reference image content to the back view through depth-guided mutual self-attention. With this method we could achieve high-fidelity and texture-consistent human rendering from a single image. Moreover to alleviate the color distortion that occurs in the side region we propose a visibility-aware patch consistency regularization combined with the synthesized back view texture. Experiments conducted on both real and synthetic data demonstrate the effectiveness of our method and show that our approach outperforms previous baseline methods.

We introduce ChatPose a framework employing Large Language Models (LLMs) to understand and reason about 3D human poses from images or textual descriptions. Our work is motivated by the human ability to intuitively understand postures from a single image or a brief description a process that intertwines image interpretation world knowledge and an understanding of body language. Traditional human pose estimation and generation methods often operate in isolation lacking semantic understanding and reasoning abilities. ChatPose addresses these limitations by embedding SMPL poses as distinct signal tokens within a multimodal LLM enabling the direct generation of 3D body poses from both textual and visual inputs. Leveraging the powerful capabilities of multimodal LLMs ChatPose unifies classical 3D human pose and generation tasks while offering user interactions. Additionally ChatPose empowers LLMs to apply their extensive world knowledge in reasoning about human poses leading to two advanced tasks: speculative pose generation and reasoning about pose estimation. These tasks involve reasoning about humans to generate 3D poses from subtle text queries possibly accompanied by images. We establish benchmarks for these tasks moving beyond traditional 3D pose generation and estimation methods. Our results show that ChatPose out-performs existing multimodal LLMs and task-specific methods on these newly proposed tasks. Furthermore ChatPose's ability to understand and generate 3D human poses based on complex reasoning opens new directions in human pose analysis. Code and data are available for research at https://yfeng95.github.io/ChatPose.

Even the best current algorithms for estimating body 3D shape and pose yield results that include body self-intersections. In this paper we present CLOAF which exploits the diffeomorphic nature of Ordinary Differential Equations to eliminate such self-intersections while still imposing body shape constraints. We show that unlike earlier approaches to addressing this issue ours completely eliminates the self-intersections without compromising the accuracy of the reconstructions. Being differentiable CLOAF can be used to fine-tune pose and shape estimation baselines to improve their overall performance and eliminate self-intersections in their predictions. Furthermore we demonstrate how our CLOAF strategy can be applied to practically any motion field induced by the user. CLOAF also makes it possible to edit motion to interact with the environment without worrying about potential collision or loss of body-shape prior.

We propose CG-HOI the first method to address the task of generating dynamic 3D human-object interactions (HOIs) from text. We model the motion of both human and object in an interdependent fashion as semantically rich human motion rarely happens in isolation without any interactions. Our key insight is that explicitly modeling contact between the human body surface and object geometry can be used as strong proxy guidance both during training and inference. Using this guidance to bridge human and object motion enables generating more realistic and physically plausible interaction sequences where the human body and corresponding object move in a coherent manner. Our method first learns to model human motion object motion and contact in a joint diffusion process inter-correlated through cross-attention. We then leverage this learned contact for guidance during inference to synthesize realistic and coherent HOIs. Extensive evaluation shows that our joint contact-based human-object interaction approach generates realistic and physically plausible sequences and we show two applications highlighting the capabilities of our method. Conditioned on a given object trajectory we can generate the corresponding human motion without re-training demonstrating strong human-object interdependency learning. Our approach is also flexible and can be applied to static real-world 3D scene scans.

Human-Object Interaction (HOI) Detection constitutes an important aspect of human-centric scene understanding which requires precise object detection and interaction recognition. Despite increasing advancement in detection recognizing subtle and intricate interactions remains challenging. Recent methods have endeavored to leverage the rich semantic representation from pre-trained CLIP yet fail to efficiently capture finer-grained spatial features that are highly informative for interaction discrimination. In this work instead of solely using representations from CLIP we fill the gap by proposing a spatial adapter that efficiently utilizes the multi-scale spatial information in the pre-trained detector. This leads to a bilateral adaptation that mutually produces complementary features. To further improve interaction recognition under occlusion which is common in crowded scenarios we propose an Occluded Part Extrapolation module that guides the model to recover the spatial details from manually occluded feature maps. Moreover we design a Conditional Contextual Mining module that further mines informative contextual clues from the spatial features via a tailored cross-attention mechanism. Extensive experiments on V-COCO and HICO-DET benchmarks demonstrate that our method significantly outperforms prior art on both standard and zero-shot settings resulting in new state-of-the-art performance. Additional ablation studies further validate the effectiveness of each component in our method.

Saliency ranking detection (SRD) has emerged as a challenging task in computer vision aiming not only to identify salient objects within images but also to rank them based on their degree of saliency. Existing SRD datasets have been created primarily using mouse-trajectory data which inadequately captures the intricacies of human visual perception. Addressing this gap this paper introduces the first large-scale SRD dataset SIFR constructed using genuine human fixation data thereby aligning more closely with real visual perceptual processes. To establish a baseline for this dataset we propose QAGNet a novel model that leverages salient instance query features from a transformer detector within a tri-tiered nested graph. Through extensive experiments we demonstrate that our approach outperforms existing state-of-the-art methods across two widely used SRD datasets and our newly proposed dataset. Code and dataset are available at https://github.com/EricDengbowen/QAGNet.

Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars real-time performance has mostly been demonstrated for static scenes only. To address this we propose ASH an animatable Gaussian splatting approach for photorealistic rendering of dynamic humans in real time. We parameterize the clothed human as animatable 3D Gaussians which can be efficiently splatted into image space to generate the final rendering. However naively learning the Gaussian parameters in 3D space poses a severe challenge in terms of compute. Instead we attach the Gaussians onto a deformable character model and learn their parameters in 2D texture space which allows leveraging efficient 2D convolutional architectures that easily scale with the required number of Gaussians. We benchmark ASH with competing methods on pose-controllable avatars demonstrating that our method outperforms existing real-time methods by a large margin and shows comparable or even better results than offline methods.

Recent progress in human shape learning shows that neural implicit models are effective in generating 3D human surfaces from limited number of views and even from a single RGB image. However existing monocular approaches still struggle to recover fine geometric details such as face hands or cloth wrinkles. They are also easily prone to depth ambiguities that result in distorted geometries along the camera optical axis. In this paper we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy. Our model learns geometric details from both multi-resolution pixel-aligned and voxel-aligned features to leverage depth information and enable spatial relationships mitigating depth ambiguities. We further enhance the quality of the reconstructed shape by introducing a depth-supervision strategy which improves the accuracy of the signed distance field estimation of points that lie on the reconstructed surface. Experiments demonstrate that ANIM outperforms state-of-the-art works that use RGB surface normals point cloud or RGB-D data as input. In addition we introduce ANIM-Real a new multi-modal dataset comprising high-quality scans paired with consumer-grade RGB-D camera and our protocol to fine-tune ANIM enabling high-quality reconstruction from real-world human capture.

Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds further advancing downstream human-centric tasks and applications. Previous works usually focus on tackling one specific task and rely on huge labeled data which has poor generalization capability. Considering that human has specific characteristics including the structural semantics of human body and the dynamics of human motions we propose a unified framework to make full use of the prior knowledge and explore the inherent features in the data itself for generalized human-centric point cloud video understanding. Extensive experiments demonstrate that our method achieves state-of-the-art performance on various human-related tasks including action recognition and 3D pose estimation. All datasets and code will be released soon.

Estimating full-body human motion via sparse tracking signals from head-mounted displays and hand controllers in 3D scenes is crucial to applications in AR/VR. One of the biggest challenges to this task is the one-to-many mapping from sparse observations to dense full-body motions which endowed inherent ambiguities. To help resolve this ambiguous problem we introduce a new framework to combine rich contextual information provided by scenes to benefit full-body motion tracking from sparse observations. To estimate plausible human motions given sparse tracking signals and 3D scenes we develop \text S ^2Fusion a unified framework fusing \underline S cene and sparse \underline S ignals with a conditional dif\underline Fusion model. \text S ^2Fusion first extracts the spatial-temporal relations residing in the sparse signals via a periodic autoencoder and then produces time-alignment feature embedding as additional inputs. Subsequently by drawing initial noisy motion from a pre-trained prior \text S ^2Fusion utilizes conditional diffusion to fuse scene geometry and sparse tracking signals to generate full-body scene-aware motions. The sampling procedure of \text S ^2Fusion is further guided by a specially designed scene-penetration loss and phase-matching loss which effectively regularizes the motion of the lower body even in the absence of any tracking signals making the generated motion much more plausible and coherent. Extensive experimental results have demonstrated that our \text S ^2Fusion outperforms the state-of-the-art in terms of estimation quality and smoothness.

3D human pose data collected in controlled laboratory settings present challenges for pose estimators that generalize across diverse scenarios. To address this domain generalization is employed. Current methodologies in domain generalization for 3D human pose estimation typically utilize adversarial training to generate synthetic poses for training. Nonetheless these approaches exhibit several limitations. First the lack of prior information about the target domain complicates the application of suitable augmentation through a single pose augmentor affecting generalization on target domains. Moreover adversarial training's discriminator tends to enforce similarity between source and synthesized poses impeding the exploration of out-of-source distributions. Furthermore the pose estimator's optimization is not exposed to domain shifts limiting its overall generalization ability. To address these limitations we propose a novel framework featuring two pose augmentors: the weak and the strong augmentors. Our framework employs differential strategies for generation and discrimination processes facilitating the preservation of knowledge related to source poses and the exploration of out-of-source distributions without prior information about target poses. Besides we leverage meta-optimization to simulate domain shifts in the optimization process of the pose estimator thereby improving its generalization ability. Our proposed approach significantly outperforms existing methods as demonstrated through comprehensive experiments on various benchmark datasets.

We propose a novel method for 3D point cloud action recognition. Understanding human actions in RGB videos has been widely studied in recent years however its 3D point cloud counterpart remains under-explored despite the clear value that 3D information may bring. This is mostly due to the inherent limitation of the point cloud data modality---lack of structure permutation invariance and varying number of points---which makes it difficult to learn a spatio-temporal representation. To address this limitation we propose the 3DinAction pipeline that first estimates patches moving in time (t-patches) as a key building block alongside a hierarchical architecture that learns an informative spatio-temporal representation. We show that our method achieves improved performance on existing datasets including DFAUST and IKEA ASM. Code is publicly available at https://github.com/sitzikbs/3dincaction

While head-mounted devices are becoming more compact they provide egocentric views with significant self-occlusions of the device user. Hence existing methods often fail to accurately estimate complex 3D poses from egocentric views. In this work we propose a new transformer-based framework to improve egocentric stereo 3D human pose estimation which leverages the scene information and temporal context of egocentric stereo videos. Specifically we utilize 1) depth features from our 3D scene reconstruction module with uniformly sampled windows of egocentric stereo frames and 2) human joint queries enhanced by temporal features of the video inputs. Our method is able to accurately estimate human poses even in challenging scenarios such as crouching and sitting. Furthermore we introduce two new benchmark datasets i.e. UnrealEgo2 and UnrealEgo-RW (RealWorld). UnrealEgo2 is a large-scale in-the-wild dataset captured in synthetic 3D scenes. UnrealEgo-RW is a real-world dataset captured with our newly developed device. The proposed datasets offer a much larger number of egocentric stereo views with a wider variety of human motions than the existing datasets allowing comprehensive evaluation of existing and upcoming methods. Our extensive experiments show that the proposed approach significantly outperforms previous methods. UnrealEgo2 UnrealEgo-RW and trained models are available on our project page and Benchmark Challenge.