PoseLess: Depth-Free Vision-to-Joint Control via Direct Image Mapping with VLM

• URL: http://arxiv.org/abs/2503.07111v2
• Date: Tue, 11 Mar 2025 02:26:42 GMT
• Title: PoseLess: Depth-Free Vision-to-Joint Control via Direct Image Mapping with VLM
• Authors: Alan Dao, Dinh Bach Vu, Tuan Le Duc Anh, Bui Quang Huy,
• Abstract summary: PoseLess is a novel framework for robot hand control that eliminates the need for explicit pose estimation by directly mapping 2D images to joint angles using projected representations.<n>Our approach leverages synthetic training data generated through randomized joint configurations, enabling zero-shot generalization to real-world scenarios and cross-morphology transfer from robotic to human hands.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces PoseLess, a novel framework for robot hand control that eliminates the need for explicit pose estimation by directly mapping 2D images to joint angles using projected representations. Our approach leverages synthetic training data generated through randomized joint configurations, enabling zero-shot generalization to real-world scenarios and cross-morphology transfer from robotic to human hands. By projecting visual inputs and employing a transformer-based decoder, PoseLess achieves robust, low-latency control while addressing challenges such as depth ambiguity and data scarcity. Experimental results demonstrate competitive performance in joint angle prediction accuracy without relying on any human-labelled dataset.

Related papers

• DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images [9.768951663960257]
  We propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits.<n>First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images.<n>Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block.
  arXiv  Detail & Related papers  (2024-12-25T06:36:24Z)
• Drive-1-to-3: Enriching Diffusion Priors for Novel View Synthesis of Real Vehicles [81.29018359825872]
  This paper consolidates a set of good practices to finetune large pretrained models for a real-world task. Specifically, we develop several strategies to account for discrepancies between the synthetic data and real driving data. Our insights lead to effective finetuning that results in a $68.8%$ reduction in FID for novel view synthesis over prior arts.
  arXiv  Detail & Related papers  (2024-12-19T03:39:13Z)
• ManipGPT: Is Affordance Segmentation by Large Vision Models Enough for Articulated Object Manipulation? [17.356760351203715]
  This paper introduces ManipGPT, a framework designed to predict optimal interaction areas for articulated objects. We created a dataset of 9.9k simulated and real images to bridge the sim-to-real gap. We significantly improved part-level affordance segmentation, adapting the model's in-context segmentation capabilities to robot manipulation scenarios.
  arXiv  Detail & Related papers  (2024-12-13T11:22:01Z)
• RoboPEPP: Vision-Based Robot Pose and Joint Angle Estimation through Embedding Predictive Pre-Training [27.63332596592781]
  Vision-based pose estimation of articulated robots with unknown joint angles has applications in collaborative robotics and human-robot interaction tasks. Current frameworks use neural network encoders to extract image features and downstream layers to predict joint angles and robot pose. We introduce RoboPEPP, a method that fuses information about the robot's physical model into the encoder using a masking-based self-supervised embedding-predictive architecture.
  arXiv  Detail & Related papers  (2024-11-26T18:26:17Z)
• Weakly-supervised 3D Pose Transfer with Keypoints [57.66991032263699]
  Main challenges of 3D pose transfer are: 1) Lack of paired training data with different characters performing the same pose; 2) Disentangling pose and shape information from the target mesh; 3) Difficulty in applying to meshes with different topologies. We propose a novel weakly-supervised keypoint-based framework to overcome these difficulties.
  arXiv  Detail & Related papers  (2023-07-25T12:40:24Z)
• Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold [79.94300820221996]
  DragGAN is a new way of controlling generative adversarial networks (GANs) DragGAN allows anyone to deform an image with precise control over where pixels go, thus manipulating the pose, shape, expression, and layout of diverse categories such as animals, cars, humans, landscapes, etc. Both qualitative and quantitative comparisons demonstrate the advantage of DragGAN over prior approaches in the tasks of image manipulation and point tracking.
  arXiv  Detail & Related papers  (2023-05-18T13:41:25Z)
• Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild [35.55753131098285]
  We propose a point cloud based hand joint tracking network, HandTrackNet, to estimate the inter-frame hand joint motion. Our pipeline then reconstructs the full hand via converting the predicted hand joints into a template-based parametric hand model MANO. For object tracking, we devise a simple yet effective module that estimates the object SDF from the first frame and performs optimization-based tracking.
  arXiv  Detail & Related papers  (2022-09-24T13:40:09Z)
• MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust Classifier [37.774220727662914]
  We propose a one-shot mask-guided image synthesis that allows controlling manipulations of a single image. Our proposed method, entitled MAGIC, leverages structured gradients from a pre-trained quasi-robust classifier. MAGIC aggregates gradients over the input, driven by a guide binary mask that enforces a strong, spatial prior.
  arXiv  Detail & Related papers  (2022-09-23T12:15:40Z)
• Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation [70.32536356351706]
  We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations. We derive suitable measures to quantify prediction uncertainty at both pose and joint level. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-29T07:14:58Z)
• End-to-End Trainable Multi-Instance Pose Estimation with Transformers [68.93512627479197]
  We propose a new end-to-end trainable approach for multi-instance pose estimation by combining a convolutional neural network with a transformer. Inspired by recent work on end-to-end trainable object detection with transformers, we use a transformer encoder-decoder architecture together with a bipartite matching scheme to directly regress the pose of all individuals in a given image. Our model, called POse Estimation Transformer (POET), is trained using a novel set-based global loss that consists of a keypoint loss, a keypoint visibility loss, a center loss and a class loss.
  arXiv  Detail & Related papers  (2021-03-22T18:19:22Z)
• Unsupervised Metric Relocalization Using Transform Consistency Loss [66.19479868638925]
  Training networks to perform metric relocalization traditionally requires accurate image correspondences. We propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.
  arXiv  Detail & Related papers  (2020-11-01T19:24:27Z)

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.