upload dataset

README.md

@@ -5,7 +5,7 @@ The official implementation of work "Diffusion4D: Fast Spatial-temporal Consiste
 [[Project Page]](https://vita-group.github.io/Diffusion4D/) | [[Video (Youtube)]](https://www.youtube.com/watch?v=XJT-cMt_xVo) | [[Video (results)]](https://www.youtube.com/watch?v=sg7uUUfpM-c) | [[视频 (Bilibili)]](https://b23.tv/ojVe6Uv) | [[Arxiv]](https://arxiv.org/abs/2405.16645) | [[Dataset]](https://huggingface.co/datasets/hw-liang/Diffusion4D)
 
 ## News
-
+- 2024.6.4:  Released rendered data from curated [objaverse-1.0](https://huggingface.co/datasets/hw-liang/Diffusion4D/tree/main/objaverse1.0_curated), including orbital videos of dynamic 3D, orbital videos of static 3D, and monocular videos from front view.
 - 2024.5.27:  Released metadata for objects and data preparation code!
 - 2024.5.26:  Released on arxiv!
 
@@ -16,7 +16,7 @@ We collect a large-scale, high-quality dynamic 3D(4D) dataset sourced from the
 vast 3D data corpus of [Objaverse-1.0](https://objaverse.allenai.org/objaverse-1.0/) and [Objaverse-XL](https://github.com/allenai/objaverse-xl). We apply a series of empirical rules to curate the source dataset. You can find more details in our [paper](https://arxiv.org/abs/2405.16645). In this part, we will release the selected 4D assets, including:
 1. Selected high-quality 4D object ID.
 2. A render script using Blender, providing optional settings to render your personalized data.
-3. (To be uploaded) Rendered 4D images by our team to save you GPU time.
+3. Rendered 4D images by our team to save you GPU time.
 
 ## 4D Dataset ID/Metadata
 We collect 365k dynamic 3D assets from Objaverse-1.0 (42k) and Objaverse-xl (323k). We curate a high-quality subset to train our models. With objaverse-1.0, we provide the selected 11k ids in `rendering/src/ObjV1_curated.txt`. Uncurated 42k IDs of all the animated objects from objaverse-1.0 are in `rendering/src/ObjV1_all_animated.txt`.