Search Results for author:
Found 13 papers, 3 papers with code
AToM: Amortized Text-to-Mesh using 2D Diffusion
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously.
Diffusion Priors for Dynamic View Synthesis from Monocular Videos
Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos.
SceneWiz3D: Towards Text-guided 3D Scene Composition
We are witnessing significant breakthroughs in the technology for generating 3D objects from text.
HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance
To address texture flickering issues in NeRFs, we introduce a kernel smoothing technique that refines importance sampling weights coarse-to-fine, ensuring accurate and thorough sampling in high-density regions.
Diffusion Probabilistic Fields
Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains.
Controllable Radiance Fields for Dynamic Face Synthesis
Recent work on 3D-aware image synthesis has achieved compelling results using advances in neural rendering.
AMICO: Amodal Instance Composition
Image composition aims to blend multiple objects to form a harmonized image.
Synthetic Power Analyses: Empirical Evaluation and Application to Cognitive Neuroimaging
We propose synthetic power analyses; a framework for estimating statistical power at various sample sizes, and empirically explore the performance of synthetic power analysis for sample size selection in cognitive neuroscience experiments.
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
Controllable semantic image editing enables a user to change entire image attributes with a few clicks, e. g., gradually making a summer scene look like it was taken in winter.
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision
EMIXER is an conditional generative adversarial model by 1) generating an image based on a label, 2) encoding the image to a hidden embedding, 3) producing the corresponding text via a hierarchical decoder from the image embedding, and 4) a joint discriminator for assessing both the image and the corresponding text.
Conditional Structure Generation through Graph Variational Generative Adversarial Nets
Graph embedding has been intensively studied recently, due to the advance of various neural network models.
FMRI data augmentation via synthesis
Thus, our results suggest that data augmentation via synthesis is a promising approach to address the limited availability of fMRI data, and to improve the quality of predictive fMRI models.
Hallucinating brains with artificial brains
Our classification results provide a quantitative evaluation of the quality of the generated images, and also serve as an additional contribution of this manuscript.
