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Official implementation of 'Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields'

active 2023-08-192025-06-26 (UTC)

Complete coverage26,572 / 26,572 hourly files (100%) · 2 absent upstream2023-08-152026-08-26 (UTC)
Events
146
Pushes
2
Pull requests
0
Issues
15
Stars
93
Forks
10

Activity over time

Daily event counts in the loaded window

Line chart, 678 days from 2023-08-19 to 2025-06-26. Pushes: 2 total, peak 1 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 15 total, peak 2 in a day. Comments: 26 total, peak 4 in a day. Stars: 93 total, peak 3 in a day.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

Recent activity

Latest issues, pull requests and releases

  • Issue comment#7NikitaVasilevN2024-09-23 07:14
    Which pretrained checkpoints to use to perform rendering?
  • Issue comment#7Jihyun05102024-09-22 02:02
    Which pretrained checkpoints to use to perform rendering?
  • Issue comment#14CrispyFeSo42024-07-02 02:56
    huggingface/tokenizers ERROR
  • Issue#14CrispyFeSo42024-07-02 01:56
    huggingface/tokenizers ERROR
  • Issue#13Vinayak-VG2024-06-10 12:32
    Regarding the number of views generated during training
  • Issue comment#12eckertzhang2024-04-24 15:53
    Hello and thank you for your wonderful work. I would like to ask again if you have done some preprocessing on the scale of the depth map inferred by the depth estimation model, because I found that it is often difficult to directly use the depth map obtained by depth estimation for warp to align the target perspective pose and depth value. The scale size greatly affects the warp operation.
  • Issue comment#3jeremy123z2024-04-08 09:03
    Smaller models
  • Issue comment#8jeremy123z2024-04-08 08:50
    Fail to generate 360 scene
  • Issue comment#6manxw2024-04-05 12:17
    Failing to install requirements
  • Issue comment#11KunHan-KH2024-04-02 23:07
    cannot use environment.yaml to install conda env
  • Issue comment#11KunHan-KH2024-04-02 23:07
    cannot use environment.yaml to install conda env
  • Issue comment#8lxy1982024-04-01 01:22
    Fail to generate 360 scene
  • Issue comment#6snowwhitewings2024-03-31 07:20
    Failing to install requirements
  • Issue comment#8Maroceannn2024-03-29 03:33
    Fail to generate 360 scene
  • Issue comment#8lxy1982024-03-29 01:54
    Fail to generate 360 scene
  • Issue comment#8lxy1982024-03-29 01:53
    Fail to generate 360 scene
  • Issue comment#8lxy1982024-03-29 01:48
    Fail to generate 360 scene
  • Issue#12wdlllllllll2024-03-28 04:03
    Hello and thank you for your wonderful work. I would like to ask again if you have done some preprocessing on the scale of the depth map inferred by the depth estimation model, because I found that it is often difficult to directly use the depth map obtained by depth estimation for warp to align the target perspective pose and depth value. The scale size greatly affects the warp operation.
  • Issue comment#11PeterSchlenker2024-03-22 22:12
    cannot use environment.yaml to install conda env
  • Issue#11yetiiil2024-03-20 10:16
    cannot use environment.yaml to install conda env
  • Issue#10jly08102024-03-20 03:17
    Hardware required for training
  • Issue comment#6yetiiil2024-03-18 16:31
    Failing to install requirements
  • Issue#9Asianfleet2024-03-08 10:18
    inference time
  • Issue comment#7NikitaVasilevN2024-03-02 09:14
    Which pretrained checkpoints to use to perform rendering?
  • Issue comment#4Asianfleet2024-03-02 08:02
    Extracting the mesh

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 93 stars here means stars gained during the window, not the repo's star count.