1 citations · 2 across the 9 of their papers we have counts for
12 papers
Streaming Multi-Track Timeline Control for 3D Human Motion Generation
Yangsong Zhang, Anujith Muraleedharan, Rikhat Akizhanov +3
Text-driven human motion generation has advanced substantially, yet most methods assume instructions are available before synthesis. Interactive applications require responding to…
PACT: End-to-End Learning of Human Pose, Contacts, and Forces from Video
Rikhat Akizhanov, Yangsong Zhang, Nikolai Kaliazin +5
Human motion, environmental contacts, and interaction forces are governed by common physical laws, yet existing approaches typically separate visual pose reconstruction from contac…
ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation
Omar El Khalifi, Thomas Rossi, Oscar Fossey +6
For artistic applications, video generation requires fine-grained control over both performance and cinematography, i.e., the actor's motion and the camera trajectory. We present A…
MessyKitchens: Contact-rich object-level 3D scene reconstruction
Junaid Ahmed Ansari, Ran Ding, Fabio Pizzati +1
Monocular 3D scene reconstruction has recently seen significant progress. Powered by the modern neural architectures and large-scale data, recent methods achieve high performance i…
PhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization
Yangsong Zhang, Anujith Muraleedharan, Rikhat Akizhanov +5
Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion data. Building on this progress, recent…
Material Palette: Extraction of Materials from a Single Image
Ivan Lopes, Fabio Pizzati, Raoul de Charette
In this paper, we propose a method to extract physically-based rendering (PBR) materials from a single real-world image. We do so in two steps: first, we map regions of the image t…
ManiFest: Manifold Deformation for Few-shot Image Translation
Fabio Pizzati, Jean-François Lalonde, Raoul de Charette
Most image-to-image translation methods require a large number of training images, which restricts their applicability. We instead propose ManiFest: a framework for few-shot image…
Leveraging Local Domains for Image-to-Image Translation
Anthony Dell'Eva, Fabio Pizzati, Massimo Bertozzi +1
Image-to-image (i2i) networks struggle to capture local changes because they do not affect the global scene structure. For example, translating from highway scenes to offroad, i2i…
Physics-informed Guided Disentanglement in Generative Networks
Fabio Pizzati, Pietro Cerri, Raoul de Charette
Image-to-image translation (i2i) networks suffer from entanglement effects in presence of physics-related phenomena in target domain (such as occlusions, fog, etc), lowering altoge…
CoMoGAN: continuous model-guided image-to-image translation
Fabio Pizzati, Pietro Cerri, Raoul de Charette
CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normaliza…
Model-based occlusion disentanglement for image-to-image translation
Fabio Pizzati, Pietro Cerri, Raoul de Charette
Image-to-image translation is affected by entanglement phenomena, which may occur in case of target data encompassing occlusions such as raindrops, dirt, etc. Our unsupervised mode…
Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation
Fabio Pizzati, Raoul de Charette, Michela Zaccaria +1
Image-to-image translation architectures may have limited effectiveness in some circumstances. For example, while generating rainy scenarios, they may fail to model typical traits…