Search Results for author:
Found 17 papers, 7 papers with code
SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing
Deep neural networks (DNNs) have achieved great successes in various vision applications due to their strong expressive power.
DreamRelation: Relation-Centric Video Customization
First, in Relational Decoupling Learning, we disentangle relations from subject appearances using relation LoRA triplet and hybrid mask training strategy, ensuring better generalization across diverse relationships.
FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free Scale Fusion
Visual diffusion models achieve remarkable progress, yet they are typically trained at limited resolutions due to the lack of high-resolution data and constrained computation resources, hampering their ability to generate high-fidelity images or videos at higher resolutions.
Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model
As a fundamental backbone for video generation, diffusion models are challenged by low inference speed due to the sequential nature of denoising.
PersonalVideo: High ID-Fidelity Video Customization without Dynamic and Semantic Degradation
The key challenge lies in maintaining high ID fidelity consistently while preserving the original motion dynamic and semantic following after the identity injection.
DreamVideo-2: Zero-Shot Subject-Driven Video Customization with Precise Motion Control
In this paper, we present DreamVideo-2, a zero-shot video customization framework capable of generating videos with a specific subject and motion trajectory, guided by a single image and a bounding box sequence, respectively, and without the need for test-time fine-tuning.
FreeTraj: Tuning-Free Trajectory Control in Video Diffusion Models
Diffusion model has demonstrated remarkable capability in video generation, which further sparks interest in introducing trajectory control into the generation process.
FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling
With the availability of large-scale video datasets and the advances of diffusion models, text-driven video generation has achieved substantial progress.
ReliTalk: Relightable Talking Portrait Generation from a Single Video
Our key insight is to decompose the portrait's reflectance from implicitly learned audio-driven facial normals and images.
Temporal Contrastive Learning for Spiking Neural Networks
However, in this work, we find the method above is not ideal for the SNNs training as it omits the temporal dynamics of SNNs and degrades the performance quickly with the decrease of inference time steps.
StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3
Notably, StyleFaceV is capable of generating realistic $1024\times1024$ face videos even without high-resolution training videos.
Text2Human: Text-Driven Controllable Human Image Generation
In this work, we present a text-driven controllable framework, Text2Human, for a high-quality and diverse human generation.
Few-shot Forgery Detection via Guided Adversarial Interpolation
Notably, our method is also validated to be robust to choices of majority and minority forgery approaches.
Can Shape Structure Features Improve Model Robustness Under Diverse Adversarial Settings?
Specifically, EdgeNetRob and EdgeGANRob first explicitly extract shape structure features from a given image via an edge detection algorithm.
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
In this paper, we aim to explore the impact of semantic manipulation on DNNs predictions by manipulating the semantic attributes of images and generate "unrestricted adversarial examples".
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs).
Precise Temporal Action Localization by Evolving Temporal Proposals
The performances of existing action localization approaches remain unsatisfactory in precisely determining the beginning and the end of an action.
