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. We identify critical design decisions–adjusted noise schedules ...
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. We identify critical design decisions–adjusted noise schedules fo...
Summary: IC3D——第一个通过图像引导生成三维形状的Image-Conditioned 3D diffusion model(第一个采用体素表示的3D DDPM Model) 在text-to-image的启发下,创建利用了pair图像-形状预训练(CISP),通过对比预训练将image-shape联合嵌入 Intro: 第一个基于单视图图像条件生成三维形状的DDPM Model.[^1] 对于选择体素的...
video (T2V) diffusion model to be conditioned on a provided image, enabling TI2V generation without any optimization, fine-tuning, or introducing external modules. Our approach leverages a pretrained T2V diffusion foundation model as the generative prior. To guide video generation with the ...
3) Text-conditioned Image Feature Generation,如图(c),跟Image captioning反过 来的步骤,输入文本词,能生成图像区域特征; 4) Image Captioning,常规的image captioning自回归loss。 Pre-train Datasets: 1) Conceptual Captions dataset Downstream Tasks:
Multiple Conditioned Image Generation, SDXL, Low-rank adaptation Refined cookbookstransformerspacesloragradiogencolab-notebookhuggingfacetexttoimageimagegenerationstable-diffusiondiffusers UpdatedDec 7, 2024 Jupyter Notebook gmickel/snapspell Sponsor Star3 ...
image generation datasets is given. The evaluation metrics that are suitable for each image generation category are discussed and a comparison of the performance of existing solutions is provided to better inform the state-of-the-art and identify their limitations and strengths. Lastly, the current...
In this paper, we consider the problem of image-to-video translation, where one or a set of input images are translated into an output video which contains motions of a single object. Especially, we focus on predicting motions conditioned by high-level structures, such as facial expression and...
Pre title: Self-conditioned Image Generation via Generating Representations accepted: arXiv 2023 paper: https://arxiv.org/abs/2312.03701 code: https:/
RCG: Self-conditioned Image Generation via Generating Representations TL; DR:将图像的无监督表征作为(自)条件(而非是将文本 prompt 作为条件),生成与原图语义内容一致的多样且高质量结果。视觉训练能不能 / 需不需要摆脱文本,仍有待研究。 引言 就像图像自监督表征学习(对比学习 / 图像掩码建模)成功赶超了有监...