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Found 24 papers, 11 papers with code
Gaussian Splashing: Dynamic Fluid Synthesis with Gaussian Splatting
We demonstrate the feasibility of integrating physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructed using 3DGS.
Animatable 3D Gaussians for High-fidelity Synthesis of Human Motions
The learnable code serves as a pose-dependent appearance embedding for refining the erroneous appearance caused by geometric transformation of Gaussians, based on which an appearance refinement model is learned to produce residual Gaussian properties to match the appearance in target pose.
PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF
We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects.
A Locality-based Neural Solver for Optical Motion Capture
Finally, we propose a training regime based on representation learning and data augmentation, by training the model on data with masking.
Adaptive Local Basis Functions for Shape Completion
Quantitative and qualitative experiments demonstrate that our method outperforms the state-of-the-art methods in shape completion, detail preservation, generalization to unseen geometries, and computational cost.
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack
Via BASAR, we find on-manifold adversarial samples are extremely deceitful and rather common in skeletal motions, in contrast to the common belief that adversarial samples only exist off-manifold.
Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks
We present a learning algorithm that uses bone-driven motion networks to predict the deformation of loose-fitting garment meshes at interactive rates.
Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction
However, as source images are often misaligned due to the large disparities among the camera settings, strong assumptions have been made in the past with respect to the camera(s) or/and the object in interest, limiting the application of such techniques.
Unsupervised Image Generation with Infinite Generative Adversarial Networks
Image generation has been heavily investigated in computer vision, where one core research challenge is to generate images from arbitrarily complex distributions with little supervision.
BASAR:Black-box Attack on Skeletal Action Recognition
The robustness of skeleton-based activity recognizers has been questioned recently, which shows that they are vulnerable to adversarial attacks when the full-knowledge of the recognizer is accessible to the attacker.
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack
In this paper, we examine the robustness of state-of-the-art action recognizers against adversarial attack, which has been rarely investigated so far.
High-order Differentiable Autoencoder for Nonlinear Model Reduction
This paper provides a new avenue for exploiting deep neural networks to improve physics-based simulation.
One-shot Face Reenactment Using Appearance Adaptive Normalization
The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance.
In-game Residential Home Planning via Visual Context-aware Global Relation Learning
In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home complex.
Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation
In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the source appearance.
Second-order Neural Network Training Using Complex-step Directional Derivative
We believe our method will inspire a wide-range of new algorithms for deep learning and numerical optimization.
Dynamic Future Net: Diversified Human Motion Generation
We evaluate our model on a wide range of motions and compare it with the state-of-the-art methods.
Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks
In this paper, we introduce a method for one-shot face reenactment, which uses the reconstructed 3D meshes (i. e., the source mesh and driving mesh) as guidance to learn the optical flow needed for the reenacted face synthesis.
AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
We base our work on the assumption that most human-made objects are constituted by parts and these parts can be well represented by generalized primitives.
Unsupervised Facial Action Unit Intensity Estimation via Differentiable Optimization
The generator learns to "render" a face image from a set of facial parameters in a differentiable way, and the feature extractor extracts deep features for measuring the similarity of the rendered image and input real image.
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database.
EmbedMask: Embedding Coupling for One-stage Instance Segmentation
The pixel-level clustering enables EmbedMask to generate high-resolution masks without missing details from repooling, and the existence of proposal embedding simplifies and strengthens the clustering procedure to achieve high speed with higher performance than segmentation-based methods.
Ranked #87 on
Instance Segmentation
on COCO test-dev
SMART: Skeletal Motion Action Recognition aTtack
In this paper, we propose a method, SMART, to attack action recognizers which rely on 3D skeletal motions.
DeepWarp: DNN-based Nonlinear Deformation
DeepWarp is an efficient and highly re-usable deep neural network (DNN) based nonlinear deformable simulation framework.
Graphics
