ๆญฃๅœจๅŠ ่ฝฝ่ง†้ข‘...

่ง†้ข‘ๅŠ ่ฝฝๅคฑ่ดฅ

(1/2) ๐Ÿ“ข๐Ÿ“ข๐ƒ๐ข๐Ÿ๐Ÿ๐ฎ๐ฌ๐ข๐จ๐ง๐€๐ฏ๐š๐ญ๐š๐ซ๐ฌ ๐Ÿ“ข๐Ÿ“ข High-fidelity 3D head avatars with precise control over viewpoint, expression, and pose. -> Our parametric 3D model enables control & consistency + 2D diffusion makes it photoreal.

66,436 ๆฌก่ง‚็œ‹ โ€ข 2 ๅนดๅ‰ โ€ขvia X (Twitter)

7 ๆก่ฏ„่ฎบ

Matthias Niessner ็š„ๅคดๅƒ
Matthias Niessner2 ๅนดๅ‰

(2/2) We propose a diffusion-based neural renderer that translates 3D renderings from a neural parametric head model (NPHM) into photorealistic images. Learnable neural features are encoded on the 3D model & enforce consistency. Awesome work by @TobiasKirschst1 with @SGiebenhain

AndaSeat ็š„ๅคดๅƒ
AndaSeat1 ๅนดๅ‰

๐ŸŽฏ Holds breath in sniper mode... ๐ŸŽฎ X-Air Pro's clutch features: ๐Ÿ’จ Breathable mesh for sweaty 1v5 moments ๐Ÿ”ซ Perfect height for that winning headshot โšก 5D armrests for precise aim control ๐Ÿ˜… "Why did I solo queue" comfort mode ๐ŸŽฏ For those who main AWP and live dangerously! ๐Ÿช‘โœจ Aim better: ๐ŸŽช Headshot deal: Snipe $20 off! #AndaSeat #CSGO #Valorant #ESports #Gaming

Hermes แฏ… ็š„ๅคดๅƒ
Hermes แฏ…2 ๅนดๅ‰

Every time I think I have a grasp on whatโ€™s possible, new ai models like this appear and just blow the lid off of my imagination.

Ankush Singal ็š„ๅคดๅƒ
Ankush Singal2 ๅนดๅ‰

I tried something similar

็”ฐไธญ็พฉๅผ˜ | taziku CEO / AI ร— Creative ็š„ๅคดๅƒ
็”ฐไธญ็พฉๅผ˜ | taziku CEO / AI ร— Creative2 ๅนดๅ‰

The detailed reproduction of facial expressions, etc., is very wonderful!

denverdash ็š„ๅคดๅƒ
denverdash2 ๅนดๅ‰

Cool! How do you do the scans?

Mind of Machine ็š„ๅคดๅƒ
Mind of Machine2 ๅนดๅ‰

@CoffeeVectors Prepare for a whole new level of catfishing ๐Ÿ˜‚

็›ธๅ…ณ่ง†้ข‘

๐Ÿ“ข๐Ÿ“ข ๐๐ž๐ซ๐œ๐‡๐ž๐š๐: ๐๐ž๐ซ๐œ๐ž๐ฉ๐ญ๐ฎ๐š๐ฅ ๐‡๐ž๐š๐ ๐Œ๐จ๐๐ž๐ฅ ๐Ÿ๐จ๐ซ ๐’๐ข๐ง๐ ๐ฅ๐ž-๐ˆ๐ฆ๐š๐ ๐ž ๐Ÿ‘๐ƒ ๐‡๐ž๐š๐ ๐‘๐ž๐œ๐จ๐ง๐ฌ๐ญ๐ซ๐ฎ๐œ๐ญ๐ข๐จ๐ง & ๐„๐๐ข๐ญ๐ข๐ง๐ ๐Ÿ“ข๐Ÿ“ข PercHead reconstructs realistic 3D heads from a single image and enables disentangled 3D editing via geometric controls and style inputs from images or text. At its core is a generalized 3D head decoder trained with perceptual supervision from DINOv2 and SAM 2.1. We find that our new perceptual loss formulation improves reconstruction fidelity compared to commonly-used methods such as LPIPS. Our trained reconstruction model is able to generate 3D-consistent heads from a single input image. Even with challenging side-view inputs, the model robustly infers missing regions for a coherent, high-fidelity output. In addition, our architecture seamlessly adapts to downstream tasks: by swapping the encoder, we can transform the model into a disentangled 3D editing pipeline. In this scenario, we can control geometry through - potentially hand-drawn - segmentation maps, and condition style via image or text prompt. We also provide an interactive GUI to enable the exploration of our editing pipeline. ๐ŸŒ ๐Ÿ“ฝ๏ธ Great work by Antonio Oroz and Tobias Kirschstein

Matthias Niessner

18,903 ๆฌก่ง‚็œ‹ โ€ข 10 ไธชๆœˆๅ‰

๐Ÿ“ข๐Ÿ“ข ๐€๐ฏ๐š๐ญ๐Ÿ‘๐ซ ๐Ÿ“ข๐Ÿ“ข Avat3r creates high-quality 3D head avatars from just a few input images in a single forward pass with a new dynamic 3DGS reconstruction model. Video: Project: Our core idea is to make Gaussian Reconstruction Models animatable. We find that a simple cross-attention to an expression code sequence is already sufficient to model complex facial expressions. We then incorporate position maps from DUSt3R and feature maps from Sapiens to facilitate the prediction task. While DUSt3R's position maps act as a pixel-aligned initialization for the Gaussians' positions, the Sapiens feature maps help the cross-view transformer to match corresponding image tokens in the 4 input images. One major challenge in creating a 3D head avatar from smartphone images comes from inconsistent facial expressions when the subject could not remain perfectly static during the capture. We eliminate this static requirement by simply showing our model input images with different facial expressions during training. This technique makes our model robust to inconsistent input images later on. Finally, we show that despite the model has been trained with 4 input images, one can even create a 3D head avatar when only a single image is available. To achieve this, we employ a pre-trained 3D GAN to lift the single image to 3D and then render the 4 input images for our model. This allows us to create 3D head avatars from single images and even highly out-of-distribution examples like AI generated faces, paintings or statues. Great work by Tobias Kirschstein from his internship at Meta with Javier Romero, Artem Sevastopolsky, and Shunsuke Saito

Matthias Niessner

74,818 ๆฌก่ง‚็œ‹ โ€ข 1 ๅนดๅ‰

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

AK

161,530 ๆฌก่ง‚็œ‹ โ€ข 2 ๅนดๅ‰