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Found 19 papers, 10 papers with code
Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks
In this paper, we study the problem of constrained min-max optimization in a black-box setting, where the desired optimizer cannot access the gradients of the objective function but may query its values.
TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation
We address the problem of regressing 3D human pose and shape from a single image, with a focus on 3D accuracy.
MonoHair: High-Fidelity Hair Modeling from a Monocular Video
For the interior, we employ a data-driven, multi-view 3D hair reconstruction method.
ChatPose: Chatting about 3D Human Pose
Additionally, ChatPose empowers LLMs to apply their extensive world knowledge in reasoning about human poses, leading to two advanced tasks: speculative pose generation and reasoning about pose estimation.
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT).
Ghost on the Shell: An Expressive Representation of General 3D Shapes
Recent work has focused on the former, and methods for reconstructing open surfaces do not support fast reconstruction with material and lighting or unconditional generative modelling.
Pairwise Similarity Learning is SimPLE
In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL).
Text-Guided Generation and Editing of Compositional 3D Avatars
We argue that existing methods are limited because they employ a monolithic modeling approach, using a single representation for the head, face, hair, and accessories.
Learning Disentangled Avatars with Hybrid 3D Representations
Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e. g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata.
Controlling Text-to-Image Diffusion by Orthogonal Finetuning
To tackle this challenge, we introduce a principled finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks.
MeshDiffusion: Score-based Generative 3D Mesh Modeling
We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation.
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm.
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network
Model-based reinforcement learning usually suffers from a high sample complexity in training the world model, especially for the environments with complex dynamics.
Model-based Reinforcement Learning
reinforcement-learning
+1
Capturing and Animation of Body and Clothing from Monocular Video
Building on this insight, we propose SCARF (Segmented Clothed Avatar Radiance Field), a hybrid model combining a mesh-based body with a neural radiance field.
Collaborative Regression of Expressive Bodies using Moderation
Second, human shape is highly correlated with gender, but existing work ignores this.
Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression.
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
In this paper, we study the problem of constrained robust (min-max) optimization ina black-box setting, where the desired optimizer cannot access the gradients of the objective function but may query its values.
DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image
Recovering 3D human body shape and pose from 2D images is a challenging task due to high complexity and flexibility of human body, and relatively less 3D labeled data.
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Ranked #1 on
3D Face Reconstruction
on Florence
