Excited to share the last chapter of my PhD! Presenting SIGMA-Gen: Structure and Identity Guided Multi-subject Image Generation accepted at ICLR 2026.
Paper:https://t.co/G8nYBJOyYZ
Page:https://t.co/pmfJaLnpcL
Demo:https://t.co/M6vOv7cUJS
#ICLR2026#ComputerVision#GenerativeAI
Continuous diffusion had a good run—now it’s time for Discrete diffusion!
Introducing Anchored Posterior Sampling (APS)
APS outperforms discrete and continuous baselines in terms of performance & scaling on inverse problems, stylization, and text-guided editing.
We are presenting YouDream today at #NeurIPS2024 at East Exhibit Hall #2706 in the morning session. Drop by and chat with both @Sandy_uta and me at the poster!
Project page: https://t.co/D8xmgnJEq1
🚨 Folks, please check out our new #ECCV2024 paper SPIRE, a semantic and restoration prompt-driven image restoration technique, that leverages natural language to control the restoration process.
https://t.co/JdfI9PmyJb
That's cool. Create posed and anatomic consistent animals with text-to-3D.
"YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals"
Project: https://t.co/ErX3smIIOZ
Paper: https://t.co/rhLW01lvin
I hope we will get some code soon to try it out!
8 papers submitted to daily papers so far today
If your a author with at least one indexed paper on HF, submit your paper directly to daily papers: https://t.co/VbXh61IzHE
YouDream can generate high-quality 3D animals from a single image and a text prompt!
The method is able to preserve anatomic consistency and is capable of generating and combining commonly found animals.
Links ⬇️
YouDream can generate high-quality 3D animals from a single image and a text prompt!
The method is able to preserve anatomic consistency and is capable of generating and combining commonly found animals.
Links ⬇️
YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals
Explained by author:
https://t.co/A3qhSAa9gO
Overview:
YouDream enhances 3D generation of anatomically controllable animals using a text-to-image diffusion model guided by 2D views of 3D poses.
Unlike previous methods that rely solely on text or images, YouDream expands creative possibilities by maintaining anatomic consistency in generated animals.
The fully automated pipeline adapts 3D poses from a limited library using a multi-agent LLM, eliminating the need for human intervention.
A user study confirms the superiority of YouDream's animal models over previous methods, highlighting its effectiveness in generating high-quality 3D animals.
Paper:
https://t.co/BLyB0JZmpN
Project Page:
https://t.co/38aMNZ9iAF
Code:
https://t.co/xN71dNe4V4
@SunnySanyal9 Thanks!! It was quite fun to work on this topic as well. We will be releasing the code real soon and would love to see what crazy assets you can create.
📣 It's finally out there. Our work YouDream is now on arXiv. Do check out the video results on the webpage: https://t.co/9X7uZwQ1Sc
And watch out for code release. ⭐️
Here’s a sneak peek of a few creative examples you can create using YouDream.
🚨 New paper
YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals
YouDream achieves multi-view consistency without being trained on 3D datasets and generates imaginary assets impossible to be made using prior methods.🧵 below.
page: https://t.co/6GJEVTfuE2
YouDream is a method to generate high quality anatomically controllable animals.
YouDream is guided using a text-to-image diffusion model controlled by 2D views of a 3D pose prior. It generates 3D animals which are not possible to create using previous text-to-3D generative methods.
Paper: YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals
Link: https://t.co/IZBcKyZq3q
Project: https://t.co/JRiQgE55M9
#AI #AI美女 #LLMs #deeplearning #machinelearning #3D #generativeAI
We have open-sourced the code and data for "Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions" @CVPR at: https://t.co/imdgtl5nwt
Also, find the poster and video links at the GitHub repo!
There's too much happening right now, so here's just a bunch of links
GPT-4 + Medprompt -> SOTA MMLU
https://t.co/Jkp96izfec
Mixtral 8x7B @ MLX nice and clean
https://t.co/75StzY5AHe
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
https://t.co/gOCWjfY7ec
Phi-2 (2.7B), the smallest most impressive model
https://t.co/Fps8tI5QVi
LLM360: Towards Fully Transparent Open-Source LLMs
https://t.co/l6E16GfdIN
Honorable mentions
https://t.co/7GQqiCGHRH
https://t.co/3GZrYPp9KP
https://t.co/Su8iiDksMZ
@venturetwins Division is a direct consequence multiplication and thus addition. Dividing a number 'y' by a certain number 'x' is equivalent to finding out how many times you need to add 'x' to get 'y'. In this case, however number of times you add 0, one can never reach the number 'y'