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Found 32 papers, 15 papers with code
MagicMirror: Fast and High-Quality Avatar Generation with a Constrained Search Space
We introduce a novel framework for 3D human avatar generation and personalization, leveraging text prompts to enhance user engagement and customization.
MVDD: Multi-View Depth Diffusion Models
State-of-the-art results from extensive experiments demonstrate MVDD's excellent ability in 3D shape generation, depth completion, and its potential as a 3D prior for downstream tasks.
Mesh-Guided Neural Implicit Field Editing
We first introduce a differentiable method using marching tetrahedra for polygonal mesh extraction from the neural implicit field and then design a differentiable color extractor to assign colors obtained from the volume renderings to this extracted mesh.
Efficient 3D Articulated Human Generation with Layered Surface Volumes
These layers are rendered using alpha compositing with fast differentiable rasterization, and they can be interpreted as a volumetric representation that allocates its capacity to a manifold of finite thickness around the template.
AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control
Neural implicit fields are powerful for representing 3D scenes and generating high-quality novel views, but it remains challenging to use such implicit representations for creating a 3D human avatar with a specific identity and artistic style that can be easily animated.
Invertible Neural Skinning
Next, we combine PIN with a differentiable LBS module to build an expressive and end-to-end Invertible Neural Skinning (INS) pipeline.
Unsupervised Volumetric Animation
We propose a novel approach for unsupervised 3D animation of non-rigid deformable objects.
InfiniCity: Infinite-Scale City Synthesis
Toward infinite-scale 3D city synthesis, we propose a novel framework, InfiniCity, which constructs and renders an unconstrainedly large and 3D-grounded environment from random noises.
3DAvatarGAN: Bridging Domains for Personalized Editable Avatars
Finally, we propose a novel inversion method for 3D-GANs linking the latent spaces of the source and the target domains.
DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis
Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects.
NeRF-Art: Text-Driven Neural Radiance Fields Stylization
As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images.
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN
Bi-stride pools nodes on every other frontier of the breadth-first search (BFS), without the need for the manual drawing of coarser meshes and avoiding the wrong edges by spatial proximity.
Cross-Modal 3D Shape Generation and Manipulation
In this paper, we propose a generic multi-modal generative model that couples the 2D modalities and implicit 3D representations through shared latent spaces.
Quantized GAN for Complex Music Generation from Dance Videos
We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos.
R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis
On the other hand, Neural Light Field (NeLF) presents a more straightforward representation over NeRF in novel view synthesis -- the rendering of a pixel amounts to one single forward pass without ray-marching.
NeROIC: Neural Rendering of Objects from Online Image Collections
We present a novel method to acquire object representations from online image collections, capturing high-quality geometry and material properties of arbitrary objects from photographs with varying cameras, illumination, and backgrounds.
CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields
Furthermore, we propose an inverse optimization method that accurately projects an input image to the latent codes for manipulation to enable editing on real images.
DisUnknown: Distilling Unknown Factors for Disentanglement Learning
In this paper, we adopt a general setting where all factors that are hard to label or identify are encapsulated as a single unknown factor.
Flow Guided Transformable Bottleneck Networks for Motion Retargeting
Human motion retargeting aims to transfer the motion of one person in a "driving" video or set of images to another person.
A Good Image Generator Is What You Need for High-Resolution Video Synthesis
We introduce a motion generator that discovers the desired trajectory, in which content and motion are disentangled.
Ranked #32 on
Video Generation
on UCF-101
Exemplar-Based 3D Portrait Stylization
In the second texture style transfer stage, we focus on performing style transfer on the canonical texture by adopting a differentiable renderer to optimize the texture in a multi-view framework.
Cross-Domain and Disentangled Face Manipulation with 3D Guidance
Face image manipulation via three-dimensional guidance has been widely applied in various interactive scenarios due to its semantically-meaningful understanding and user-friendly controllability.
Motion Representations for Articulated Animation
To facilitate animation and prevent the leakage of the shape of the driving object, we disentangle shape and pose of objects in the region space.
Ranked #1 on
Video Reconstruction
on Tai-Chi-HD (512)
Diverse Semantic Image Synthesis via Probability Distribution Modeling
In this paper, we propose a novel diverse semantic image synthesis framework from the perspective of semantic class distributions, which naturally supports diverse generation at semantic or even instance level.
Ranked #1 on
Image-to-Image Translation
on ADE20K Labels-to-Photos
(LPIPS metric)
Efficient Semantic Image Synthesis via Class-Adaptive Normalization
Spatially-adaptive normalization (SPADE) is remarkably successful recently in conditional semantic image synthesis \cite{park2019semantic}, which modulates the normalized activation with spatially-varying transformations learned from semantic layouts, to prevent the semantic information from being washed away.
MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing
In this paper, we present MichiGAN (Multi-Input-Conditioned Hair Image GAN), a novel conditional image generation method for interactive portrait hair manipulation.
Interactive Video Stylization Using Few-Shot Patch-Based Training
In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of the sequence.
Neural Hair Rendering
Unlike existing supervised translation methods that require model-level similarity to preserve consistent structure representation for both real images and fake renderings, our method adopts an unsupervised solution to work on arbitrary hair models.
Human Motion Transfer from Poses in the Wild
In this paper, we tackle the problem of human motion transfer, where we synthesize novel motion video for a target person that imitates the movement from a reference video.
Rethinking Spatially-Adaptive Normalization
Despite its impressive performance, a more thorough understanding of the true advantages inside the box is still highly demanded, to help reduce the significant computation and parameter overheads introduced by these new structures.
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning
In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective.
End-to-End Time-Lapse Video Synthesis from a Single Outdoor Image
Time-lapse videos usually contain visually appealing content but are often difficult and costly to create.
