Search Results for author:
Found 14 papers, 5 papers with code
Motion-Conditioned Image Animation for Video Editing
We introduce MoCA, a Motion-Conditioned Image Animation approach for video editing.
Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning
We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video conditioned on the text and the generated image.
Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation
However, existing approaches are limited by their reliance on relatively small-scale motion capture data, leading to poor performance on more diverse, in-the-wild prompts.
Ranked #25 on
Motion Synthesis
on HumanML3D
Text-Conditional Contextualized Avatars For Zero-Shot Personalization
Recent large-scale text-to-image generation models have made significant improvements in the quality, realism, and diversity of the synthesized images and enable users to control the created content through language.
Shape-Guided Diffusion with Inside-Outside Attention
We introduce precise object silhouette as a new form of user control in text-to-image diffusion models, which we dub Shape-Guided Diffusion.
More Control for Free! Image Synthesis with Semantic Diffusion Guidance
We investigate fine-grained, continuous control of this model class, and introduce a novel unified framework for semantic diffusion guidance, which allows either language or image guidance, or both.
Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottleneck GAN model for unconditional synthesis of complex scenes.
Semantic Bottleneck Scene Generation
For the former, we use an unconditional progressive segmentation generation network that captures the distribution of realistic semantic scene layouts.
Ranked #1 on
Image Generation
on Cityscapes-5K 256x512
Compositional GAN (Extended Abstract): Learning Image-Conditional Binary Composition
Generative Adversarial Networks (GANs) can produce images of surprising complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene.
Discriminator Rejection Sampling
We propose a rejection sampling scheme using the discriminator of a GAN to approximately correct errors in the GAN generator distribution.
Compositional GAN: Learning Image-Conditional Binary Composition
Generative Adversarial Networks (GANs) can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene.
Multi-Content GAN for Few-Shot Font Style Transfer
In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface.
Learning Detection with Diverse Proposals
To predict a set of diverse and informative proposals with enriched representations, this paper introduces a differentiable Determinantal Point Process (DPP) layer that is able to augment the object detection architectures.
Auxiliary Image Regularization for Deep CNNs with Noisy Labels
Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs).
