Official implementation of "Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting"
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Updated
May 30, 2024 - Python
Official implementation of "Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting"
A collection of resources on controllable generation with text-to-image diffusion models.
[CVPR 2024] Official implementation of "Towards Realistic Scene Generation with LiDAR Diffusion Models"
[ICML 2023] Official PyTorch Implementation of "Hierarchical Neural Coding for Controllable CAD Model Generation".
Stay upto date with recent papers in the field!
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
[ACL2023] NeuroStructural Decoding: Neural Text Generation with Structural Constraints
[EMNLP2022] BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples
[Preprint] AdaVAE: Exploring Adaptive GPT-2s in VAEs for Language Modeling PyTorch Implementation
This is the official implementation of CCLAP for Controllable Chinese Landscape Painting generation. (ICME2023, oral)
Update-to-data resources for conditional content generation, including human motion generation, image or video generation and editing.
Caption-Anything is a versatile tool combining image segmentation, visual captioning, and ChatGPT, generating tailored captions with diverse controls for user preferences. https://huggingface.co/spaces/TencentARC/Caption-Anything https://huggingface.co/spaces/VIPLab/Caption-Anything
Code for CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
Implementation of Collage Diffusion (https://arxiv.org/abs/2303.00262)
Controllable mage captioning model with unsupervised modes
[KBS] PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation PyTorch Implementation
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
[Paperlist] Awesome paper list of controllable text generation via latent auto-encoders. Contributions of any kind are welcome.
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