7 citations · 21 across the 13 of their papers we have counts for
20 papers
FUSION: Full-Body Unified Motion Prior for Body and Hands via Diffusion
Enes Duran, Nikos Athanasiou, Muhammed Kocabas +2
Hands are central to interacting with our surroundings and conveying gestures, making their inclusion essential for full-body motion synthesis. Despite this, existing human motion…
BEDLAM2.0: Synthetic Humans and Cameras in Motion
Joachim Tesch, Giorgio Becherini, Prerana Achar +4
Inferring 3D human motion from video remains a challenging problem with many applications. While traditional methods estimate the human in image coordinates, many applications requ…
PromptHMR: Promptable Human Mesh Recovery
Yufu Wang, Yu Sun, Priyanka Patel +3
Human pose and shape (HPS) estimation presents challenges in diverse scenarios such as crowded scenes, person-person interactions, and single-view reconstruction. Existing approach…
HMP: Hand Motion Priors for Pose and Shape Estimation from Video
Enes Duran, Muhammed Kocabas, Vasileios Choutas +2
Understanding how humans interact with the world necessitates accurate 3D hand pose estimation, a task complicated by the hand's high degree of articulation, frequent occlusions, s…
HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video
Zicong Fan, Maria Parelli, Maria Eleni Kadoglou +4
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 met…
HUGS: Human Gaussian Splats
Muhammed Kocabas, Jen-Hao Rick Chang, James Gabriel +2
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…
PACE: Human and Camera Motion Estimation from in-the-wild Videos
Muhammed Kocabas, Ye Yuan, Pavlo Molchanov +5
We present a method to estimate human motion in a global scene from moving cameras. This is a highly challenging task due to the coupling of human and camera motions in the video.…
Physically Plausible Full-Body Hand-Object Interaction Synthesis
Jona Braun, Sammy Christen, Muhammed Kocabas +2
We propose a physics-based method for synthesizing dexterous hand-object interactions in a full-body setting. While recent advancements have addressed specific facets of human-obje…
Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors
Xi Wang, Gen Li, Yen-Ling Kuo +3
We present a method for inferring diverse 3D models of human-object interactions from images. Reasoning about how humans interact with objects in complex scenes from a single 2D im…
TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning
Andrea Ziani, Zicong Fan, Muhammed Kocabas +2
We introduce TempCLR, a new time-coherent contrastive learning approach for the structured regression task of 3D hand reconstruction. Unlike previous time-contrastive methods for h…
ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation
Zicong Fan, Omid Taheri, Dimitrios Tzionas +4
Humans intuitively understand that inanimate objects do not move by themselves, but that state changes are typically caused by human manipulation (e.g., the opening of a book). Thi…
Human-Aware Object Placement for Visual Environment Reconstruction
Hongwei Yi, Chun-Hao P. Huang, Dimitrios Tzionas +5
Humans are in constant contact with the world as they move through it and interact with it. This contact is a vital source of information for understanding 3D humans, 3D scenes, an…
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
Sammy Christen, Muhammed Kocabas, Emre Aksan +3
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.…
Learning to Regress Bodies from Images using Differentiable Semantic Rendering
Sai Kumar Dwivedi, Nikos Athanasiou, Muhammed Kocabas +1
Learning to regress 3D human body shape and pose (e.g.~SMPL parameters) from monocular images typically exploits losses on 2D keypoints, silhouettes, and/or part-segmentation when…
SPEC: Seeing People in the Wild with an Estimated Camera
Muhammed Kocabas, Chun-Hao P. Huang, Joachim Tesch +3
Due to the lack of camera parameter information for in-the-wild images, existing 3D human pose and shape (HPS) estimation methods make several simplifying assumptions: weak-perspec…
Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation
Zicong Fan, Adrian Spurr, Muhammed Kocabas +3
In natural conversation and interaction, our hands often overlap or are in contact with each other. Due to the homogeneous appearance of hands, this makes estimating the 3D pose of…
PARE: Part Attention Regressor for 3D Human Body Estimation
Muhammed Kocabas, Chun-Hao P. Huang, Otmar Hilliges +1
Despite significant progress, we show that state of the art 3D human pose and shape estimation methods remain sensitive to partial occlusion and can produce dramatically wrong pred…
VIBE: Video Inference for Human Body Pose and Shape Estimation
Muhammed Kocabas, Nikos Athanasiou, Michael J. Black
Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce…
Self-Supervised Learning of 3D Human Pose using Multi-view Geometry
Muhammed Kocabas, Salih Karagoz, Emre Akbas
Training accurate 3D human pose estimators requires large amount of 3D ground-truth data which is costly to collect. Various weakly or self supervised pose estimation methods have…
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
Muhammed Kocabas, Salih Karagoz, Emre Akbas
In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method. MultiPoseNet ca…