Tech
AI method reconstructs 3D scene details from simulated images using inverse rendering
Over the past decades, computer scientists have developed many computational tools that can analyze and interpret images. These tools have proved useful for a broad range of applications, including robotics, autonomous driving, health care, manufacturing and even entertainment.
Most of the best performing computer vision approaches employed to date rely on so-called feed-forward neural networks. These are computational models that process input images step by step, ultimately making predictions about them.
While some of these models were found to perform well when tested on the data they analyzed during training, they often do not generalize well across new images and in different scenarios. In addition, their predictions and the patterns they extract from images can be difficult to interpret.
Researchers at Princeton University recently developed a new inverse rendering approach that is more transparent and could also interpret a wide range of images more reliably. The new approach, introduced in a paper published in Nature Machine Intelligence, relies on a generative artificial intelligence (AI)-based method to simulate the process of image creation, while also optimizing it by gradually adjusting a model’s internal parameters.
“Generative AI and neural rendering have transformed the field in recent years for creating novel content: producing images or videos from scene descriptions,” Felix Heide, senior author of the paper, told Tech Xplore. “We investigate whether we can flip this around and use these generative models for extracting the scene descriptions from images.”
The new approach developed by Heide and his colleagues relies on a so-called differentiable rendering pipeline. This is a process for the simulation of image creation, relying on compressed representations of images created by generative AI models.
“We developed an analysis-by-synthesis approach that allows us to solve vision tasks, such as tracking, as test-time optimization problems,” explained Heide. “We found that this method generalizes across datasets, and in contrast to existing supervised learning methods, does not need to be trained on new datasets.”
Essentially, the method developed by the researchers works by placing models of 3D objects in a virtual scene depicting real world settings. These models of objects are generated by a generative AI based on random sample of 3D scene parameters.
“We then render all these objects back together into a 2D image,” said Heide. “Next, we compare this rendered image with the real observed image. Based on how different they are, we backpropagate the difference through both the differentiable rendering function and the 3D generation model to update its inputs. In just a few steps, we optimize these inputs to make the rendered match the observed images better.”
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Optimizing 3D models through inverse neural rendering. From left to right: the observed image, initial random 3D generations, and three optimization steps that refine these to better match the observed image. The observed images are faded to show the rendered objects clearly. The method effectively refines object appearance and position, all done at test time with inverse neural rendering. Credit: Ost et al.
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Generalization of 3D multi-object tracking with Inverse Neural Rendering. The method directly generalizes across datasets such as the nuScenes and Waymo Open Dataset benchmarks without additional fine-tuning and is trained on synthetic 3D models only. The observed images are overlaid with the closest generated object and tracked 3D bounding boxes. Credit: Ost et al.
A notable advantage of the team’s newly proposed approach is that it allows very generic 3D object generation models trained on synthetic data to perform well across a wide range of datasets containing images captured in real-world settings. In addition, the renderings produced by the models are far more explainable than those produced by conventional rendering tools based on feed-forward machine learning models.
“Our inverse rendering approach for tracking works just as well as learned feed-forward approaches, but it provides us with explicit 3D explanations of its perceived world,” said Heide.
“The other interesting aspect is the generalization capabilities. Without changing the 3D generation model or training it on new data, our 3D multi-object tracking through Inverse Neural Rendering works well across different autonomous driving datasets and object types. This can significantly reduce the cost of fine-tuning on new data or at least work as an auto-labeling pipeline.”
This recent study could soon help to advance AI models for computer vision, improving their performance in real-world settings while also increasing their transparency. The researchers now plan to continue improving their method and start testing it on more computer vision-related tasks.
“A logical next step is the expansion of the proposed approach to other perception tasks, such as 3D detection and 3D segmentation,” added Heide. “Ultimately, we want to explore if inverse rendering can even be used to infer the whole 3D scene, and not just individual objects. This would allow our future robots to reason and continuously optimize a three-dimensional model of the world, which comes with built-in explainability.”
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More information:
Julian Ost et al, Towards generalizable and interpretable three-dimensional tracking with inverse neural rendering, Nature Machine Intelligence (2025). DOI: 10.1038/s42256-025-01083-x.
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AI method reconstructs 3D scene details from simulated images using inverse rendering (2025, August 23)
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With over 5,000 employees and 3,000 sewing machines across 90 sewing lines, VTL Group specialises in jersey knits and denim, producing up to 20 million garments per year for world-renowned brands such as Lacoste, Adidas, G-Star, Hugo Boss, Replay and Paul & Shark. The company operates six garment production units, along with dedicated facilities for screen printing, knitting, dyeing and textile finishing. This extensive vertical integration gives VTL complete control over quality, lead-times and cost-efficiency, which is vital for meeting the stringent demands of its global customer base.
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GSDCost, Coats Digital’s method analysis and pre-determined times solution, is widely acknowledged as the de-facto international standard across the sewn products industry. It supports a more collaborative, transparent, and sustainable supply chain in which brands and manufacturers establish and optimise ‘International Standard Time Benchmarks’ using standard motion codes and predetermined times. This shared framework supports accurate cost prediction, fact-based negotiation, and a more efficient garment manufacturing process, while concurrently delivering on CSR commitments.
Key Benefits and ROI for VTL Group
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Note: The headline, insights, and image of this press release may have been refined by the Fibre2Fashion staff; the rest of the content remains unchanged.
Fibre2Fashion News Desk (HU)
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Tencia Benavidez, a Supernatural user who lives in New Mexico, started her VR workouts during the Covid pandemic. She has been a regular user in the five years since, calling the ability to workout in VR ideal, given that she lives in a rural area where it’s hard to get to a gym or workout outside during a brutal winter. She stuck with Supernatural because of the community and the eagerness of Supernatural’s coaches.
“They seem like really authentic individuals that were not talking down to you,” Benavidez says. “There’s just something really special about those coaches.”
Meta bought Supernatural in 2022, folding it into its then-heavily invested in metaverse efforts. The purchase was not a smooth process, as it triggered a lengthy legal battle in which the US Federal Trade Commission tried to block Meta from purchasing the service due to antitrust concerns about Meta “trying to buy its way to the top” of the VR market. Meta ultimately prevailed. At the time, some Supernatural users were cautiously optimistic, hoping that big bag of Zuckerbucks could keep its workout juggernaut afloat.
“Meta fought the government to buy this thing,” Benavidez says. “All that just for them to shut it down? What was the point?”
I reached out to Meta and Supernatural, and neither responded to my requests for comment.
Waking Up to Ash and Dust
On Tuesday, Bloomberg reported that Meta has laid off more than 1,000 people across its VR and metaverse efforts. The move comes after years of the company hemorrhaging billions of dollars on its metaverse products. In addition to laying off most of the staff at Supernatural, Meta has shut down three internal VR studios that made games like Resident Evil 4 and Deadpool VR.
“If it was a bottom line thing, I think they could have charged more money,” Goff Johnson says about Supernatural. “I think people would have paid for it. This just seems unnecessarily heartless.”
There is a split in the community about who will stay and continue to pay the subscription fee, and who will leave. Supernatural still has more than 3,000 lessons available in the service, so while new content won’t be added, some feel there is plenty of content left in the library. Other users worry about how Supernatural will continue to license music from big-name bands.
“Supernatural is amazing, but I am canceling it because of this,” Chip told me. “The library is large, so there’s enough to keep you busy, but not for the same price.”
There are other VR workout experiences like FitXR or even the VR staple Beat Saber, which Supernatural cribs a lot of design concepts from. Still, they don’t hit the same bar for many of the Supernatural faithful.
“I’m going to stick it out until they turn the lights out on us,” says Stefanie Wong, a Bay Area accountant who has used Supernatural since shortly after the pandemic and has organized and attended meetup events. “It’s not the app. It’s the community and it’s the coaches that we really, really care about.”
Welcome to the New Age
I tried out Supernatural’s Together feature on Wednesday, the day after the layoffs. It’s where I met Chip and Alisa. When we could stop to catch our breath, we talked about the changes coming to the service. They had played through previous sessions hosted by Jane Fonda or playlists with a mix of music that would change regularly. It seems the final collaboration in Supernatural’s multiplayer mode will be what we played now, an artist series featuring entirely Imagine Dragons songs.
In the session, as we punched blocks while being serenaded by this shirtless dude crooning, recorded narrations from Supernatural coach Dwana Olsen chimed in to hype us up.
“Take advantage of these moments,” Olsen said as we punched away. “Use these movements to remind you of how much awesome life you have yet to live.”
Frankly, it was downright invigorating. And bittersweet. We ended another round, sweaty, huffing and puffing. Chip, Alisa, and I high-fived like crazy and readied for another round.
“Beautiful,” Alisa said. “It’s just beautiful, isn’t it?”
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