Search Results for author: Javier Romero

Found 31 papers, 16 papers with code

On the generalization of learned constraints for ASP solving in temporal domains

no code implementations29 Jan 2024 Javier Romero, Torsten Schaub, Klaus Strauch

Our question is now whether a constraint learned for particular time steps can be generalized and reused at other time stamps, and ultimately whether this enhances the overall solver performance on temporal problems.

URHand: Universal Relightable Hands

no code implementations10 Jan 2024 Zhaoxi Chen, Gyeongsik Moon, Kaiwen Guo, Chen Cao, Stanislav Pidhorskyi, Tomas Simon, Rohan Joshi, Yuan Dong, Yichen Xu, Bernardo Pires, He Wen, Lucas Evans, Bo Peng, Julia Buffalini, Autumn Trimble, Kevyn McPhail, Melissa Schoeller, Shoou-I Yu, Javier Romero, Michael Zollhöfer, Yaser Sheikh, Ziwei Liu, Shunsuke Saito

To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities.

From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations

1 code implementation3 Jan 2024 Evonne Ng, Javier Romero, Timur Bagautdinov, Shaojie Bai, Trevor Darrell, Angjoo Kanazawa, Alexander Richard

We present a framework for generating full-bodied photorealistic avatars that gesture according to the conversational dynamics of a dyadic interaction.


Drivable 3D Gaussian Avatars

no code implementations14 Nov 2023 Wojciech Zielonka, Timur Bagautdinov, Shunsuke Saito, Michael Zollhöfer, Justus Thies, Javier Romero

We present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats.

Diffusion Shape Prior for Wrinkle-Accurate Cloth Registration

no code implementations10 Nov 2023 Jingfan Guo, Fabian Prada, Donglai Xiang, Javier Romero, Chenglei Wu, Hyun Soo Park, Takaaki Shiratori, Shunsuke Saito

Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data.

Human Body Measurement Estimation with Adversarial Augmentation

no code implementations11 Oct 2022 Nataniel Ruiz, Miriam Bellver, Timo Bolkart, Ambuj Arora, Ming C. Lin, Javier Romero, Raja Bala

Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes.

Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing

no code implementations30 Jun 2022 Donglai Xiang, Timur Bagautdinov, Tuur Stuyck, Fabian Prada, Javier Romero, Weipeng Xu, Shunsuke Saito, Jingfan Guo, Breannan Smith, Takaaki Shiratori, Yaser Sheikh, Jessica Hodgins, Chenglei Wu

The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry.

plingo: A system for probabilistic reasoning in clingo based on lpmln

1 code implementation23 Jun 2022 Susana Hahn, Tomi Janhunen, Roland Kaminski, Javier Romero, Nicolas Rühling, Torsten Schaub

We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes.

Embodied Hands: Modeling and Capturing Hands and Bodies Together

no code implementations7 Jan 2022 Javier Romero, Dimitrios Tzionas, Michael J. Black

We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H).

Answer Set Programming Made Easy

no code implementations11 Nov 2021 Jorge Fandinno, Seemran Mishra, Javier Romero, Torsten Schaub

We take up an idea from the folklore of Answer Set Programming, namely that choices, integrity constraints along with a restricted rule format is sufficient for Answer Set Programming.

Planning with Incomplete Information in Quantified Answer Set Programming

1 code implementation13 Aug 2021 Jorge Fandinno, François Laferrière, Javier Romero, Torsten Schaub, Tran Cao Son

We present a general approach to planning with incomplete information in Answer Set Programming (ASP).


Learning First-Order Representations for Planning from Black-Box States: New Results

no code implementations23 May 2021 Ivan D. Rodriguez, Blai Bonet, Javier Romero, Hector Geffner

For this, the learning problem is formulated as the search for a simplest first-order domain description D that along with information about instances I_i (number of objects and initial state) determine state space graphs G(P_i) that match the observed state graphs G_i where P_i = (D, I_i).

SMPLpix: Neural Avatars from 3D Human Models

1 code implementation16 Aug 2020 Sergey Prokudin, Michael J. Black, Javier Romero

Recent advances in deep generative models have led to an unprecedented level of realism for synthetically generated images of humans.

How to build your own ASP-based system?!

1 code implementation15 Aug 2020 Roland Kaminski, Javier Romero, Torsten Schaub, Philipp Wanko

This is arguably due to its attractive modeling-grounding-solving workflow that provides an easy approach to problem solving, even for laypersons outside computer science.

eclingo: A solver for Epistemic Logic Programs

no code implementations5 Aug 2020 Pedro Cabalar, Jorge Fandinno, Javier Garea, Javier Romero, Torsten Schaub

The input language of eclingo uses the syntax extension capabilities of clingo to define subjective literals that, as usual in epistemic logic programs, allow for checking the truth of a regular literal in all or in some of the answer sets of a program.

Learning Multi-Human Optical Flow

2 code implementations24 Oct 2019 Anurag Ranjan, David T. Hoffmann, Dimitrios Tzionas, Siyu Tang, Javier Romero, Michael J. Black

Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset.

Optical Flow Estimation

FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second

no code implementations ICCV 2019 David Smith, Matthew Loper, Xiaochen Hu, Paris Mavroidis, Javier Romero

Counterintuitively, the main loss which drives FAX is on per-pixel surface normals instead of per-pixel depth, making it possible to estimate detailed body geometry without any depth supervision.


Efficient Learning on Point Clouds with Basis Point Sets

1 code implementation ICCV 2019 Sergey Prokudin, Christoph Lassner, Javier Romero

The basis point set representation is a residual representation that can be computed efficiently and can be used with standard neural network architectures and other machine learning algorithms.

BIG-bench Machine Learning

Learning Human Optical Flow

1 code implementation14 Jun 2018 Anurag Ranjan, Javier Romero, Michael J. Black

Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods.

Optical Flow Estimation

Dynamic FAUST: Registering Human Bodies in Motion

no code implementations CVPR 2017 Federica Bogo, Javier Romero, Gerard Pons-Moll, Michael J. Black

We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology.

A simple yet effective baseline for 3d human pose estimation

14 code implementations ICCV 2017 Julieta Martinez, Rayat Hossain, Javier Romero, James J. Little

Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels.

3D Pose Estimation Monocular 3D Human Pose Estimation

On human motion prediction using recurrent neural networks

8 code implementations CVPR 2017 Julieta Martinez, Michael J. Black, Javier Romero

Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality.

Human motion prediction Human Pose Forecasting +3

Unite the People: Closing the Loop Between 3D and 2D Human Representations

2 code implementations CVPR 2017 Christoph Lassner, Javier Romero, Martin Kiefel, Federica Bogo, Michael J. Black, Peter V. Gehler

With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body.

 Ranked #1 on Monocular 3D Human Pose Estimation on Human3.6M (Use Video Sequence metric)

3D human pose and shape estimation Monocular 3D Human Pose Estimation

Learning from Synthetic Humans

2 code implementations CVPR 2017 Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev, Cordelia Schmid

In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data.

2D Human Pose Estimation 3D Human Pose Estimation +2

Coupling Adaptive Batch Sizes with Learning Rates

1 code implementation15 Dec 2016 Lukas Balles, Javier Romero, Philipp Hennig

The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates.

Image Classification Stochastic Optimization

Detailed Full-Body Reconstructions of Moving People From Monocular RGB-D Sequences

no code implementations ICCV 2015 Federica Bogo, Michael J. Black, Matthew Loper, Javier Romero

The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation.

FAUST: Dataset and Evaluation for 3D Mesh Registration

no code implementations CVPR 2014 Federica Bogo, Javier Romero, Matthew Loper, Michael J. Black

We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments.


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