Search Results for author:
Found 16 papers, 10 papers with code
AutoDecoding Latent 3D Diffusion Models
We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core.
Unsupervised Volumetric Animation
We propose a novel approach for unsupervised 3D animation of non-rigid deformable objects.
ScanEnts3D: Exploiting Phrase-to-3D-Object Correspondences for Improved Visio-Linguistic Models in 3D Scenes
The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural language to real-world 3D data.
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.
Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation
Diffusion probabilistic models (DPMs) have become a popular approach to conditional generation, due to their promising results and support for cross-modal synthesis.
Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation
With the aim of obtaining interpretable and controllable scene representations, our model couples learnt scene-specific feature volumes with a scene agnostic neural rendering network.
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.
Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning
In addition, our model can extract visual information as suggested by the text prompt, e. g., "an object in image one is moving northeast", and generate corresponding videos.
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.
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
Monocular Real-Time Volumetric Performance Capture
We present the first approach to volumetric performance capture and novel-view rendering at real-time speed from monocular video, eliminating the need for expensive multi-view systems or cumbersome pre-acquisition of a personalized template model.
Intuitive, Interactive Beard and Hair Synthesis with Generative Models
We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challenging hair in images of clean-shaven subjects.
Transformable Bottleneck Networks
We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN).
Realistic Dynamic Facial Textures From a Single Image Using GANs
By retargeting the PCA expression geometry from the source, as well as using the newly inferred texture, we can both animate the face and perform video face replacement on the source video using the target appearance.
