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Found 16 papers, 6 papers with code
Multiple-Crop Human Mesh Recovery with Contrastive Learning and Camera Consistency in A Single Image
We tackle the problem of single-image Human Mesh Recovery (HMR).
Incorporating Exemplar Optimization into Training with Dual Networks for Human Mesh Recovery
(2) We devise a dual-network architecture to convey the novel training paradigm, which is composed of a main regression network and an auxiliary network, in which we can formulate the exemplar optimization loss function in the same form as the training loss function.
Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection
In previous work, the two models are closely entangled with each other, and it is not known how to upgrade their method without modifying their training framework significantly.
Disentangled Representation Learning for Controllable Person Image Generation
Unlike the existing works leveraging the semantic masks to obtain the representation of each component, we propose to generate disentangled latent code via a novel attribute encoder with transformers trained in a manner of curriculum learning from a relatively easy step to a gradually hard one.
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
Based on this disentanglement, face swapping can be simplified as style and mask swapping.
Pyramid Texture Filtering
We present a simple but effective technique to smooth out textures while preserving the prominent structures.
Learning Dynamic Style Kernels for Artistic Style Transfer
Arbitrary style transfer has been demonstrated to be efficient in artistic image generation.
Fine-Grained Face Swapping via Regional GAN Inversion
We rethink face swapping from the perspective of fine-grained face editing, \textit{i. e., ``editing for swapping'' (E4S)}, and propose a framework that is based on the explicit disentanglement of the shape and texture of facial components.
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space
In this paper, we propose a novel sampling strategy for sampling very diverse results from an imbalanced multimodal distribution learned by a deep generative model.
Ranked #2 on
Human Pose Forecasting
on HumanEva-I
Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction
This motivates us to propose a novel two-stage prediction framework, including an init-prediction network that just computes the good guess and then a formal-prediction network that predicts the target future poses based on the guess.
Ranked #5 on
Human Pose Forecasting
on Human3.6M
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction
The extracted features at each scale are then combined and decoded to obtain the residuals between the input and target poses.
Ranked #7 on
Human Pose Forecasting
on Human3.6M
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction
In this paper, we propose $\text{HF}^2$-VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection.
Ranked #1 on
Video Anomaly Detection
on ShanghaiTech Campus
Dual Illumination Estimation for Robust Exposure Correction
By performing dual illumination estimation, we obtain two intermediate exposure correction results for the input image, with one fixes the underexposed regions and the other one restores the overexposed regions.
Enhancing Underexposed Photos using Perceptually Bidirectional Similarity
To obtain high-quality results free of these artifacts, we present a novel underexposed photo enhancement approach that is able to maintain the perceptual consistency.
Understanding More about Human and Machine Attention in Deep Neural Networks
With three example computer vision tasks, diverse representative backbones, and famous architectures, corresponding real human gaze data, and systematically conducted large-scale quantitative studies, we quantify the consistency between artificial attention and human visual attention and offer novel insights into existing artificial attention mechanisms by giving preliminary answers to several key questions related to human and artificial attention mechanisms.
L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction
Current face alignment algorithms can robustly find a set of landmarks along face contour.
