82 citations · 93 across the 12 of their papers we have counts for
15 papers
Towards in-the-wild Egocentric 3D Hand-Object Pose Estimation
Siddhant Bansal, Zhifan Zhu, Shashank Tripathi +3
Estimating accurate 3D hand-object pose from in-the-wild egocentric RGB remains challenging due to severe occlusions and ambiguous contact. Existing learning-based methods often st…
UniCon3R: Unified Contact-aware 4D Human-Scene Reconstruction from Monocular Video
Tanuj Sur, Shashank Tripathi, Nikos Athanasiou +4
We introduce UniCon3R, a unified feed-forward framework for online human-scene 4D reconstruction from monocular video. Current feed-forward human-scene reconstruction methods suffe…
PICO: Reconstructing 3D People In Contact with Objects
Alpár Cseke, Shashank Tripathi, Sai Kumar Dwivedi +4
Recovering 3D Human-Object Interaction (HOI) from single color images is challenging due to depth ambiguities, occlusions, and the huge variation in object shape and appearance. Th…
InteractVLM: 3D Interaction Reasoning from 2D Foundational Models
Sai Kumar Dwivedi, Dimitrije Antić, Shashank Tripathi +4
We introduce InteractVLM, a novel method to estimate 3D contact points on human bodies and objects from single in-the-wild images, enabling accurate human-object joint reconstructi…
SDFit: 3D Object Pose and Shape by Fitting a Morphable SDF to a Single Image
Dimitrije Antić, Georgios Paschalidis, Shashank Tripathi +3
Recovering 3D object pose and shape from a single image is a challenging and ill-posed problem. This is due to strong (self-)occlusions, depth ambiguities, the vast intra- and inte…
HUMOS: Human Motion Model Conditioned on Body Shape
Shashank Tripathi, Omid Taheri, Christoph Lassner +3
Generating realistic human motion is essential for many computer vision and graphics applications. The wide variety of human body shapes and sizes greatly impacts how people move.…
DECO: Dense Estimation of 3D Human-Scene Contact In The Wild
Shashank Tripathi, Agniv Chatterjee, Jean-Claude Passy +3
Understanding how humans use physical contact to interact with the world is key to enabling human-centric artificial intelligence. While inferring 3D contact is crucial for modelin…
Emotional Speech-Driven Animation with Content-Emotion Disentanglement
Radek Daněček, Kiran Chhatre, Shashank Tripathi +3
To be widely adopted, 3D facial avatars must be animated easily, realistically, and directly from speech signals. While the best recent methods generate 3D animations that are sync…
3D Human Pose Estimation via Intuitive Physics
Shashank Tripathi, Lea Müller, Chun-Hao P. Huang +3
Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore the fact that bodies are typically supported by the…
MIME: Human-Aware 3D Scene Generation
Hongwei Yi, Chun-Hao P. Huang, Shashank Tripathi +3
Generating realistic 3D worlds occupied by moving humans has many applications in games, architecture, and synthetic data creation. But generating such scenes is expensive and labo…
PERI: Part Aware Emotion Recognition In The Wild
Akshita Mittel, Shashank Tripathi
Emotion recognition aims to interpret the emotional states of a person based on various inputs including audio, visual, and textual cues. This paper focuses on emotion recognition…
Occluded Human Mesh Recovery
Rawal Khirodkar, Shashank Tripathi, Kris Kitani
Top-down methods for monocular human mesh recovery have two stages: (1) detect human bounding boxes; (2) treat each bounding box as an independent single-human mesh recovery task.…
AGORA: Avatars in Geography Optimized for Regression Analysis
Priyanka Patel, Chun-Hao P. Huang, Joachim Tesch +3
While the accuracy of 3D human pose estimation from images has steadily improved on benchmark datasets, the best methods still fail in many real-world scenarios. This suggests that…
PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge Distillation
Shashank Tripathi, Siddhant Ranade, Ambrish Tyagi +1
Recovering 3D human pose from 2D joints is a highly unconstrained problem. We propose a novel neural network framework, PoseNet3D, that takes 2D joints as input and outputs 3D skel…
Learning to Generate Synthetic Data via Compositing
Shashank Tripathi, Siddhartha Chandra, Amit Agrawal +3
We present a task-aware approach to synthetic data generation. Our framework employs a trainable synthesizer network that is optimized to produce meaningful training samples by ass…