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Found 15 papers, 7 papers with code
GUESS:GradUally Enriching SyntheSis for Text-Driven Human Motion Generation
The whole text-driven human motion synthesis problem is then divided into multiple abstraction levels and solved with a multi-stage generation framework with a cascaded latent diffusion model: an initial generator first generates the coarsest human motion guess from a given text description; then, a series of successive generators gradually enrich the motion details based on the textual description and the previous synthesized results.
Ranked #6 on
Motion Synthesis
on KIT Motion-Language
Towards Detailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model
Besides, we introduce a multi-denoiser framework for the advanced diffusion model to ease the learning of high-dimensional model and fully explore the generative potential of the diffusion model.
WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on
Image-based Virtual Try-On (VITON) aims to transfer an in-shop garment image onto a target person.
Fashion Matrix: Editing Photos by Just Talking
The utilization of Large Language Models (LLMs) for the construction of AI systems has garnered significant attention across diverse fields.
GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning
Specifically, compared with the previous global warping mechanism, LFGP employs local flows to warp garments parts individually, and assembles the local warped results via the global garment parsing, resulting in reasonable warped parts and a semantic-correct intact garment even with challenging inputs. On the other hand, our DGT training strategy dynamically truncates the gradient in the overlap area and the warped garment is no more required to meet the boundary constraint, which effectively avoids the texture squeezing problem.
Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning
Existing methods are restricted in this setting as they estimate garment warping flows mainly based on 2D poses and appearance, which omits the geometric prior of the 3D human body shape.
ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design
ARMANI discretizes an image into uniform tokens based on a learned cross-modal codebook in its first stage and uses a Transformer to model the distribution of image tokens for a real image given the tokens of the control signals in its second stage.
PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on
In this work, we take a step forwards to explore versatile virtual try-on solutions, which we argue should possess three main properties, namely, they should support unsupervised training, arbitrary garment categories, and controllable garment editing.
Dressing in the Wild by Watching Dance Videos
While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person misalignment as well as noticeable degradation in fine texture details.
Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN
Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential.
Image Comes Dancing with Collaborative Parsing-Flow Video Synthesis
The integration of human parsing and appearance flow effectively guides the generation of video frames with realistic appearance.
M3D-VTON: A Monocular-to-3D Virtual Try-On Network
Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value.
WAS-VTON: Warping Architecture Search for Virtual Try-on Network
Despite recent progress on image-based virtual try-on, current methods are constraint by shared warping networks and thus fail to synthesize natural try-on results when faced with clothing categories that require different warping operations.
Fashion Editing with Adversarial Parsing Learning
Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value.
Discovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identification
With the discovered underlying person structure, the RLD method builds a bridge between the global and local feature representation and thus improves the capacity of feature representation for person re-ID.
