FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion Models

• URL: http://arxiv.org/abs/2405.05216v1
• Date: Wed, 8 May 2024 17:09:03 GMT
• Title: FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion Models
• Authors: Jinglin Xu, Yijie Guo, Yuxin Peng,
• Abstract summary: 3D Human Pose Estimation task uses 2D images or videos to predict human joint coordinates in 3D space. We present a new Fine-Grained Prompt-Driven Denoiser based on a diffusion model for 3D HPE, named textbfFinePOSE. It consists of three core blocks enhancing the reverse process of the diffusion model. Experiments on public single-human pose datasets show that FinePOSE outperforms state-of-the-art methods.
• Score: 40.966197115577344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The 3D Human Pose Estimation (3D HPE) task uses 2D images or videos to predict human joint coordinates in 3D space. Despite recent advancements in deep learning-based methods, they mostly ignore the capability of coupling accessible texts and naturally feasible knowledge of humans, missing out on valuable implicit supervision to guide the 3D HPE task. Moreover, previous efforts often study this task from the perspective of the whole human body, neglecting fine-grained guidance hidden in different body parts. To this end, we present a new Fine-Grained Prompt-Driven Denoiser based on a diffusion model for 3D HPE, named \textbf{FinePOSE}. It consists of three core blocks enhancing the reverse process of the diffusion model: (1) Fine-grained Part-aware Prompt learning (FPP) block constructs fine-grained part-aware prompts via coupling accessible texts and naturally feasible knowledge of body parts with learnable prompts to model implicit guidance. (2) Fine-grained Prompt-pose Communication (FPC) block establishes fine-grained communications between learned part-aware prompts and poses to improve the denoising quality. (3) Prompt-driven Timestamp Stylization (PTS) block integrates learned prompt embedding and temporal information related to the noise level to enable adaptive adjustment at each denoising step. Extensive experiments on public single-human pose estimation datasets show that FinePOSE outperforms state-of-the-art methods. We further extend FinePOSE to multi-human pose estimation. Achieving 34.3mm average MPJPE on the EgoHumans dataset demonstrates the potential of FinePOSE to deal with complex multi-human scenarios. Code is available at https://github.com/PKU-ICST-MIPL/FinePOSE_CVPR2024.

Related papers

• InteractVLM: 3D Interaction Reasoning from 2D Foundational Models [85.76211596755151]
  We introduce InteractVLM, a novel method to estimate 3D contact points on human bodies and objects from single in-the-wild images. Existing methods rely on 3D contact annotations collected via expensive motion-capture systems or tedious manual labeling. We propose a new task called Semantic Human Contact estimation, where human contact predictions are conditioned explicitly on object semantics.
  arXiv  Detail & Related papers  (2025-04-07T17:59:33Z)
• Adapting Human Mesh Recovery with Vision-Language Feedback [17.253535686451897]
  We leverage vision-language models to generate interactive body part descriptions. We train a text encoder and a pose VQ-VAE, aligning texts to body poses in a shared latent space. The model can produce poses with accurate 3D perception and image consistency.
  arXiv  Detail & Related papers  (2025-02-06T07:42:00Z)
• AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring [49.78120051062641]
  3D visual grounding aims to correlate a natural language description with the target object within a 3D scene. Existing approaches commonly encounter a shortage of text3D pairs available for training. We propose AugRefer, a novel approach for advancing 3D visual grounding.
  arXiv  Detail & Related papers  (2025-01-16T09:57:40Z)
• Enhancing 3D Human Pose Estimation Amidst Severe Occlusion with Dual Transformer Fusion [13.938406073551844]
  This paper introduces a Dual Transformer Fusion (DTF) algorithm, a novel approach to obtain a holistic 3D pose estimation. To enable precise 3D Human Pose Estimation, our approach leverages the innovative DTF architecture, which first generates a pair of intermediate views. Our approach outperforms existing state-of-the-art methods on both datasets, yielding substantial improvements.
  arXiv  Detail & Related papers  (2024-10-06T18:15:27Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation [67.56268991234371]
  OV-Uni3DETR achieves the state-of-the-art performance on various scenarios, surpassing existing methods by more than 6% on average. Code and pre-trained models will be released later.
  arXiv  Detail & Related papers  (2024-03-28T17:05:04Z)
• UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning [29.037799937729687]
  2D and 3D Human Pose Estimation (HPE) are two critical perceptual tasks in computer vision. We propose UniHPE, a unified Human Pose Estimation pipeline, which aligns features from all three modalities. Our proposed method holds immense potential to advance the field of computer vision and contribute to various applications.
  arXiv  Detail & Related papers  (2023-11-24T21:55:34Z)
• Weakly Supervised 3D Open-vocabulary Segmentation [104.07740741126119]
  We tackle the challenges in 3D open-vocabulary segmentation by exploiting pre-trained foundation models CLIP and DINO in a weakly supervised manner. We distill the open-vocabulary multimodal knowledge and object reasoning capability of CLIP and DINO into a neural radiance field (NeRF) A notable aspect of our approach is that it does not require any manual segmentation annotations for either the foundation models or the distillation process.
  arXiv  Detail & Related papers  (2023-05-23T14:16:49Z)
• ScanERU: Interactive 3D Visual Grounding based on Embodied Reference Understanding [67.21613160846299]
  Embodied Reference Understanding (ERU) is first designed for this concern. New dataset called ScanERU is constructed to evaluate the effectiveness of this idea.
  arXiv  Detail & Related papers  (2023-03-23T11:36:14Z)
• DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model [25.223801390996435]
  This paper focuses on reconstructing a 3D pose from a single 2D keypoint detection. We build a novel diffusion-based framework to effectively sample diverse 3D poses from an off-the-shelf 2D detector. We evaluate our method on the widely adopted Human3.6M and HumanEva-I datasets.
  arXiv  Detail & Related papers  (2022-12-06T07:22:20Z)
• KTN: Knowledge Transfer Network for Learning Multi-person 2D-3D Correspondences [77.56222946832237]
  We present a novel framework to detect the densepose of multiple people in an image. The proposed method, which we refer to Knowledge Transfer Network (KTN), tackles two main problems. It simultaneously maintains feature resolution and suppresses background pixels, and this strategy results in substantial increase in accuracy.
  arXiv  Detail & Related papers  (2022-06-21T03:11:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.