31 citations · 42 across the 8 of their papers we have counts for
11 papers
Positional Biases Shift as Inputs Approach Context Window Limits
Blerta Veseli, Julian Chibane, Mariya Toneva +1
Large Language Models (LLMs) often struggle to use information across long inputs effectively. Prior work has identified positional biases, such as the Lost in the Middle (LiM) eff…
TriDi: Trilateral Diffusion of 3D Humans, Objects, and Interactions
Ilya A. Petrov, Riccardo Marin, Julian Chibane +1
Modeling 3D human-object interaction (HOI) is a problem of great interest for computer vision and a key enabler for virtual and mixed-reality applications. Existing methods work in…
Unimotion: Unifying 3D Human Motion Synthesis and Understanding
Chuqiao Li, Julian Chibane, Yannan He +3
We introduce Unimotion, the first unified multi-task human motion model capable of both flexible motion control and frame-level motion understanding. While existing works control a…
Object pop-up: Can we infer 3D objects and their poses from human interactions alone?
Ilya A. Petrov, Riccardo Marin, Julian Chibane +1
The intimate entanglement between objects affordances and human poses is of large interest, among others, for behavioural sciences, cognitive psychology, and Computer Vision commun…
Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes
Julian Chibane, Francis Engelmann, Tuan Anh Tran +1
Current 3D segmentation methods heavily rely on large-scale point-cloud datasets, which are notoriously laborious to annotate. Few attempts have been made to circumvent the need fo…
Interaction Replica: Tracking Human-Object Interaction and Scene Changes From Human Motion
Vladimir Guzov, Julian Chibane, Riccardo Marin +4
Our world is not static and humans naturally cause changes in their environments through interactions, e.g., opening doors or moving furniture. Modeling changes caused by humans is…
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes
Julian Chibane, Aayush Bansal, Verica Lazova +1
Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction. State-of-the-Art methods,…
Neural Unsigned Distance Fields for Implicit Function Learning
Julian Chibane, Aymen Mir, Gerard Pons-Moll
In this work we target a learnable output representation that allows continuous, high resolution outputs of arbitrary shape. Recent works represent 3D surfaces implicitly with a Ne…
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Alexandre Saint, Anis Kacem, Kseniya Cherenkova +7
The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D…
Implicit Feature Networks for Texture Completion from Partial 3D Data
Julian Chibane, Gerard Pons-Moll
Prior work to infer 3D texture use either texture atlases, which require uv-mappings and hence have discontinuities, or colored voxels, which are memory inefficient and limited in…
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion
Julian Chibane, Thiemo Alldieck, Gerard Pons-Moll
While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some…