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Jon Barron

Jon Barron Add us as a preferred source on Google

Bluesky @jonbarron.bsky.social
⚔ Compare ☆ Follow Own this channel?
🔄 Data last refreshed 3 months ago
Followers
3.8K
Account age
3 yrs
🎁 Free analysisfor Jon Barron
🕵️Fake Followers CheckReal-audience % & bot risk→ 📊Engagement rateBenchmarked vs. peers→ 💰What they chargeFair sponsored-post price→

Known for

I just pushed a new paper to arXiv. I realized that a lot of my previous work on robust losses and nerf-y things was dancing around something simpler: a slight tweak to the classic Box-Cox power transform that makes it much more useful and stable. It's this f(x, λ) here:133 views I just pushed a new paper to arXiv. I realized that a lot of my previous work on robust losses and nerf-y things was dancing around something simpler: a slight tweak to the classic Box-Cox power transform that makes it much more useful and stable. It's this f(x, λ) here: A thread of thoughts on radiance fields, from my keynote at 3DV: Radiance fields have had 3 distinct generations. First was NeRF: just posenc and a tiny MLP. This was slow to train but worked really well, and it was unusually compressed --- The NeRF was smaller than the images.114 views A thread of thoughts on radiance fields, from my keynote at 3DV: Radiance fields have had 3 distinct generations. First was NeRF: just posenc and a tiny MLP. This was slow to train but worked really well, and it was unusually compressed --- The NeRF was smaller than the images. Next week is the one year anniversary of this paper showing that videos generated from Sora are nearly 3D-consistent. I'm surprised we never saw any follow-up papers in this line evaluating other videos models this way, it would be helpful to track these metrics over time. arxiv.org/abs/2402.1740345 views Next week is the one year anniversary of this paper showing that videos generated from Sora are nearly 3D-consistent. I'm surprised we never saw any follow-up papers in this line evaluating other videos models this way, it would be helpful to track these metrics over time. arxiv.org/abs/2402.17403 Here's Bolt3D: fast feed-forward 3D generation from one or many input images. Diffusion means that generated scenes contain lots of interesting structure in unobserved regions. ~6 seconds to generate, renders in real time. Project page: szymanowiczs.github.io/bolt3d Arxiv: arxiv.org/abs/2503.1444541 views Here's Bolt3D: fast feed-forward 3D generation from one or many input images. Diffusion means that generated scenes contain lots of interesting structure in unobserved regions. ~6 seconds to generate, renders in real time. Project page: szymanowiczs.github.io/bolt3d Arxiv: arxiv.org/abs/2503.14445

📊 Post engagement

Recent posts · likes + reposts
Jan 10latest
42
Avg engagement / post
11–45
Typical post (engagement)
1.1%
Engagement vs followers
56/100
Consistent reach
Oct 2023
On Bluesky since
Half of recent posts land between 11 and 45 likes + reposts (median 38) — a dependable floor a sponsored post can count on.

🔥 Top post: I just pushed a new paper to arXiv. I realized that a lot of my · 133 likes + reposts

📊 Activity & format

Posting cadence
0.78 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly video
Recent: 3 text · 4 image · 5 video.
Follower / following
23×
Follows 165 back. A strong ratio — an audience that follows them, not a follow-for-follow network.

Recent posts

View on Bluesky ↗
▶ 🔥 Top post I just pushed a new paper to arXiv. I realized that a lot of my previous work on robust losses and nerf-y things was dancing around something simpler: a slight tweak to the classic Box-Cox power transform that makes it much more useful and stable. It's this f(x, λ) here: ★ 133
Here's a recording of my 3DV keynote from a couple weeks ago. If you're already familiar with my research, I recommend skipping to ~22 minutes in where I get to the fun stuff (whether or not 3D has been bitter-lesson'ed by video generation… ★ 71 A thread of thoughts on radiance fields, from my keynote at 3DV: Radiance fields have had 3 distinct generations. First was NeRF: just posenc and a tiny MLP. This was slow to train but worked really well, and it was unusually compressed -… ★ 114 ▶ Here's Bolt3D: fast feed-forward 3D generation from one or many input images. Diffusion means that generated scenes contain lots of interesting structure in unobserved regions. ~6 seconds to generate, renders in real time. Project page: s… ★ 41 I made this handy cheat sheet for the jargon that 6DOF math maps to for cameras and vehicles. Worth learning if you, like me, are worried about embarrassing yourself in front of a cinematographer or naval admiral. ★ 38 Can someone point me to a video of renderings of some 3DGS-like algorithm *as it is being optimized*? I want to see all those little Gaussians wobbling around. ★ 4 Next week is the one year anniversary of this paper showing that videos generated from Sora are nearly 3D-consistent. I'm surprised we never saw any follow-up papers in this line evaluating other videos models this way, it would be helpful… ★ 45 Veo 2 now has a public price point: $0.50 per second. Very important number to keep in mind when considering the future of generative and non-generative media. Taken from cloud.google.com/vertex-ai/ge... ★ 11 My pitch for an "LLM-native" alternative to citation count/h-index etc: 1) Train an LLM on a new paper and record the average loss during training. 2) Evaluate the retrained LLM on benchmarks. Your Google Scholar records avg_train_loss * b… ★ 4 ▶ fun test for image and video generation systems: add "the camera is upside down" to the prompt (especially for shots of people) and then vertically mirror the output. Even the best models struggle, with upside-down teeth and blinks, and gr… ★ 13 ▶ I fed the "spinning dancer" illusion (a silhouette of a spinning figure that can be seen as rotating clockwise or counter-clockwise, left) into Runway Gen-3 (right). It resolved the ambiguity by having the dancer face the camera and oscill… ★ 22 ▶ `A 1960s NASA scientist with a white button down shirt and black heavy rimmed glasses, with a giant thick alien umbilical cord coming out of the back of his body. The cord is holding him up in space, and he is levitating around his office.… ★ 9

🦋 Handle & authenticity

Bluesky handle
@jonbarron.bsky.social
A default .bsky.social handle — genuine, but not domain-verified. Confirm identity before a high-value deal.
On Bluesky since
Oct 2023
An early adopter from the invite-only beta (before the Feb 2024 public launch) — an original, established account.

💡 Facts

🗓️Joined Bluesky in 2023 — 3 years ago.
👁️Averages 42 views per post.
📤Posts about 0.8× per week.

🕵️ Fake follower check

Estimated
86/100
Excellent Credibility score
99%
Real Real audience
Low Fake-follower risk
High Data confidence
  • Est. 99% real, active audience · Low fake-follower risk.
  • Strong engagement (~1.1% of followers engage each post) — an active, real audience.
  • Organic base — far more followers than accounts it follows.
  • Established account (3+ years old).

Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

Principal research scientist at Google DeepMind. Synthesized views are my own. 📍SF Bay Area 🔗 http://jonbarron.info This feed is a mostly-incomplete mirror of https://x.com/jon_barron, I recommend you just follow me there.

📸 Gallery

A thread of thoughts on radiance fields, from my keynote at 3DV: Radiance fields have had 3 distinct generations. First was NeRF: just posenc and a tiny MLP. This was slow to train but worked really well, and it was unusually compressed --- The NeRF was smaller than the images. Here's Bolt3D: fast feed-forward 3D generation from one or many input images. Diffusion means that generated scenes contain lots of interesting structure in unobserved regions. ~6 seconds to generate, renders in real time. Project page: szymanowiczs.github.io/bolt3d Arxiv: arxiv.org/abs/2503.14445 I made this handy cheat sheet for the jargon that 6DOF math maps to for cameras and vehicles. Worth learning if you, like me, are worried about embarrassing yourself in front of a cinematographer or naval admiral. Next week is the one year anniversary of this paper showing that videos generated from Sora are nearly 3D-consistent. I'm surprised we never saw any follow-up papers in this line evaluating other videos models this way, it would be helpful to track these metrics over time. arxiv.org/abs/2402.17403 Veo 2 now has a public price point: $0.50 per second. Very important number to keep in mind when considering the future of generative and non-generative media. Taken from cloud.google.com/vertex-ai/ge... I just pushed a new paper to arXiv. I realized that a lot of my previous work on robust losses and nerf-y things was dancing around something simpler: a slight tweak to the classic Box-Cox power transform that makes it much more useful and stable. It's this f(x, λ) here: fun test for image and video generation systems: add "the camera is upside down" to the prompt (especially for shots of people) and then vertically mirror the output. Even the best models struggle, with upside-down teeth and blinks, and gravity tugging everything up. Here's Veo 2. I fed the "spinning dancer" illusion (a silhouette of a spinning figure that can be seen as rotating clockwise or counter-clockwise, left) into Runway Gen-3 (right). It resolved the ambiguity by having the dancer face the camera and oscillate, which is kinda clever.

🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

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