4D-LRM is a 4D content generation tool for the ByteDance Dream AI platform. It supports the generation of dynamic 3D models from a single picture or video input, and is suitable for game assets, film and television visual effects, and product display scenarios.

4D-LRM: Image-driven 4D dynamic model generation tool

Core parameters and statistics

Projects	Information
Tool name	4D-LRM
Development Team	ByteDance Dream AI
Core capabilities	Single picture/short video → 4D dynamic three-dimensional model
Delivery form	Web online service (i.e. Meng AI platform)
Input format	Single picture (it is recommended that the subject is centered and the background is clean) or short video
Output format	Dynamic 3D model with time dimension (exported in standard 3D format)
Underlying Architecture	Transformer Large Reconstruction Model (LRM)
Generation speed	Seconds to minutes
Price model	Free trial points + subscription payment (unified point system)
Platform entrance	Web browser access to Jimeng AI
Home	CN (ByteDance)

The core innovation of 4D-LRM is to break through the traditional three-dimensional reconstruction framework of "only outputting static models" and introduce the time dimension based on the LRM (Large Reconstruction Model) architecture, so that the generated results have reasonable dynamic performance - rotation, swing, deformation or more complex motion trajectories. Users only need to provide a picture or a short video, and within seconds to minutes, they can obtain dynamic 3D assets that can be viewed from multiple angles and exported for use in game engines and rendering software. This is not a scientific research demo, but a functional module of the ByteDance Dream AI platform for direct use by ordinary users.

User and market recognition

4D-LRM relies on the Jimeng AI platform for mass users. Jimeng AI itself is ByteDance's core product in the field of AI visual content generation, covering image generation, video generation, 3D/4D model generation and other capabilities. Jimeng AI's existing user base provides a natural distribution channel for 4D-LRM - after users use the image or video generation function in the platform, they can directly import the output materials into 4D-LRM for three-dimensional processing, forming a complete link from 2D creation to 3D assets.

In the technical community, 4D-LRM represents the evolutionary direction from "single image to 3D" to "single image to 4D (dynamic 3D)". Similar technologies Zero-1-to-3 and DreamFusion are mainly aimed at researchers and technical users with local deployment capabilities, while 4D-LRM minimizes the threshold by using it online on the Web. Compared with Luma AI (which supports the generation of 3D/4D assets from videos, with App and Web), the advantage of 4D-LRM lies in its deep integration with the Jimeng AI platform - users do not need to transfer materials between multiple tools.

Cost advantage

Cost Dimension	4D-LRM (Dream AI)	Traditional 3D Modeling	Luma AI	Description
Time-consuming to produce a single asset	Minutes	Hours to days	Minutes	AI method efficiency overwhelms traditional
Professional skill requirements	None	Proficient in Maya/Blender/C4D required	None	Learning cost approaches zero
Production cost	Point consumption (about $0.5-2/time)	Labor cost $100-500/day	Pay-per-view/subscription	AI solution significantly reduced
Iterative trial and error cost	Extremely low (immediate restart)	High (work hours for each modification > hours)	Low	Quick verification of ideas

For individual creators and small studios, the core cost advantage of 4D-LRM is "replacing manual time with computing time." In the traditional process, a simple 3D dynamic asset may take 2-5 days from modeling UV expansion, material mapping to binding animation, and the outsourcing cost is between ￥2000-5000. 4D-LRM compresses it to the minute level, and the entire process is controllable. For rapid iteration in the conceptual design phase—one idea, one draft, instant generation of dynamic 3D previews—the implicit value of this efficiency gain far exceeds the direct production cost savings.

Main functions

• Single image to 4D model: Upload a static image containing a clear subject (people, animals, objects are acceptable), AI automatically infers the three-dimensional structure of the object (the back and side parts that are not visible in the single image are completed a priori by the model based on the training data), and generates reasonable dynamic performance - the natural swing of the character, the slow rotation of the product, the breathing movement of the creature, etc. Dynamic effects present a "living" visual effect while maintaining target recognition.
• Video-driven reconstruction: Input a 3-10 second short video of subject dynamics, and the model extracts a more accurate three-dimensional structure and motion trajectory from multi-frame information. Compared with single image mode, the video input provides more perspective and motion information, and the geometric accuracy and dynamic naturalness of the output results are significantly higher.
• Multi-angle 360° viewing: The generated results support mouse drag and rotation in the browser, and users can observe the complete shape and dynamic details of the model from any angle. The preview is based on WebGL real-time rendering, no need to install additional plug-ins.
• Dynamic Effect Parameter Adjustment: Adjust the motion amplitude (swing size, rotation speed) and rhythm (fast/slow) of the generated results. This function allows users to fine-tune dynamic performance without regenerating, so that assets can adapt to the display needs of different scenarios.
• Standard 3D format export: The generated 4D model can be exported to standard 3D formats such as FBX or glTF, and directly imported into Unity, Unreal Engine or Blender for further processing - adding materials, adjusting animation curves or integrating into a larger scene.
• Multi-object scene support (under planning): The current version is mainly aimed at a single subject, and the processing capabilities of multi-object interaction scenes are being iterated.

Model and version evolution

Version/Phase	Time	Core Changes
LRM basic technology	~2024-2025	ByteDance releases LRM (Large Reconstruction Model) to achieve rapid reconstruction from single images to 3D
4D Extension	~2026-01	Extend the time dimension based on LRM to support the generation of dynamic 3D assets from pictures/videos
Current version	~2026-07	Web version available on Jimeng AI platform, supports click generation and export

The evolution path of 4D-LRM clearly shows the process from technical paper to product functionality. LRM was originally an academic achievement published by the ByteDance research team (core contribution: 3D reconstruction of a single image within seconds), and was later commercialized on the Jimeng AI platform. 4D-LRM is a natural extension of the LRM route—since a 3D structure can be reconstructed from a single image, it is theoretically possible to infer the changes in the structure on the timeline from a single image or multi-frame video. The platform side adopts an online grayscale release mode, so users can obtain continuous improvements in model capabilities without manual upgrades.

Technical advantages

The core architecture is based on Transformer’s Large Reconstruction Model. Compared with traditional NeRF (Neural Radiance Fields) or 3D Gaussian Splatting, 4D-LRM has significant differences in the following dimensions:

• Inference Speed: NeRF-like methods typically require hours of training/rendering time (retraining an implicit neural field for each new scene), while LRM generates a 3D representation directly through forward propagation at inference time, and a single generation can be completed in seconds to minutes. This is because LRM pre-learns a general "image → 3D structure" mapping function on large-scale training data, and does not require retraining when inferring new inputs.
• End-to-end generation: 4D-LRM adopts an encoder-decoder end-to-end architecture - the input image is encoded into a feature vector by ViT (Vision Transformer), and the decoder directly outputs a three-dimensional representation (including geometry, texture and time dimensions). There is no need for multi-stage pipeline splicing (such as depth estimation first → then point cloud reconstruction → then meshing → then binding animation), which reduces the loss of information and error accumulation in the middle.
• Temporal Dimension Modeling: The core of the 4D extension is the introduction of temporal encoding in the 3D reconstruction decoder, allowing the model to output a 3D state sequence for each frame instead of a single static frame. The model uses video data containing dynamic objects during training, and learns prior knowledge of "reasonable deformation patterns of objects on the timeline."
• Infrastructure Advantages: The model is trained on ByteDance’s internal GPU cluster, which has the infrastructure capability to process large-scale and diverse training data (covering categories such as people, animals, daily objects, buildings, etc.).

How to use

Steps	Action	Instructions
1	Open Jimeng AI official website	Register/log in to ByteDance account
2	Enter the "4D Generation" function	Select in the function list
3	Upload material	A single picture (it is recommended that the subject be centered and the background is clean) or a short video of 3-10 seconds
4	Select Parameters	Generate Quality Preset (Quick Preview / High Quality)
5	Click Generate	Wait for model inference to complete (seconds to minutes)
6	Preview and adjustment	3D preview window for multi-angle viewing and adjustment of dynamic parameters
7	Export	Export to FBX or glTF format

Image Selection Suggestions: Use white or solid-color background images with clear subjects, distinct edges, and clean backgrounds to achieve the highest segmentation and reconstruction quality for 4D-LRM. Pictures with complex backgrounds or occluded subjects may result in incomplete reconstruction results. For scenes with specific action requirements, the video input mode is preferred - video provides multi-frame motion information, and the model can more accurately capture dynamic trajectories.

Product Pricing

Level	Core benefits	Applicable users
Free quota	New users are given initial points, supporting 3-5 4D generation attempts	Experience users
Basic subscription	Monthly point package, about 20-50 times/month	Individual creator/small studio
Premium subscription	Larger point package + high-quality rendering + priority queuing	Professional users/high-frequency usage teams

The Dream AI platform adopts a unified point system - image generation, video generation, 4D/3D model generation share the same point pool. Each 4D generation consumes more points than image generation and less than high-quality video generation. The specific point consumption is related to the generation resolution, complexity and rendering quality. The platform regularly launches limited-time activities and package discounts. It is recommended to check the latest price page of the official website before subscribing.

Application scenarios

• Game asset rapid prototyping: Game artists use 4D-LRM to directly generate the first draft of dynamic models from concept drawings, which is used for level white model construction and quick verification of gameplay. A typical use case: The designer drew a concept sketch of a "magical creature that can float" → uploaded it to 4D-LRM to generate a dynamic 3D model → imported it into Unity to test the animation performance → confirm the direction before entering into precision model production. This process compresses the upfront exploration cycle from 3-5 days to 1-2 hours.
• E-commerce product 3D display: The e-commerce operator uploads real photos of the product, generates a rotatable and dynamically displayed 3D model, and embeds it on the product details page. Compared with static images, dynamic 3D display can significantly improve user browsing time and purchase conversion rate - according to public data from e-commerce platforms, the conversion rate of product details pages with 3D display increases by about 20-40%.
• Film and television pre-visual effects preview (Pre-viz): The director or visual effects team generates dynamic three-dimensional assets from the storyboard images for previewing camera movement, scene scheduling and character movement. Before official shooting or special effects production, using AI-generated dynamic models to build preview scenes can significantly reduce communication costs and rework rates in the actual shooting stage.
• Short video special effects material: Short video creators use the generated dynamic models as visual special effects materials to embed content into the content to increase picture differentiation and visual appeal. For example, turn product images into 3D dynamic displays in review videos, or add dynamic fantasy creatures to creative content.

Applicable people

• Game and film and television art practitioners: Professional creators who need to quickly produce a large number of 3D models for early concept verification and level white model construction. 4D-LRM can significantly reduce the manpower investment in pre-production - conceptual visualization that originally required the participation of 3D art is compressed into the scope of "one picture".
• E-commerce operations and visual designers: Teams that need to provide dynamic display materials for products but lack 3D software skills. 4D-LRM's "Upload → Generate → Export" process allows an e-commerce operations specialist to independently complete the production of product 3D display materials.
• Indie Game Developer: Individual or small team with limited budget. For independent developers who need dynamic 3D assets but cannot afford to outsource or hire 3D artists, 4D-LRM provides an extremely low-cost alternative - while the output accuracy is not directly usable in final game assets, it is fully usable during prototyping and early testing stages.
• AI Technology Enthusiasts and Creators: Individual users who are interested in cutting-edge generation technology use 4D-LRM to explore the creative possibilities of "Pictures → Dynamic 3D" and produce works of art or social media content.

Not suitable for boundaries: The current 4D-LRM is not suitable for professional scenarios that require strict geometric accuracy - such as industrial modeling (part assembly tolerances need to reach the 0.1mm level), medical three-dimensional reconstruction (accuracy requirements for tissue boundaries), and complex dynamic displays that require specific physical simulation effects (cloth simulation, fluid mechanics, etc.). The geometric accuracy of the generated results may not be enough in AAA games or movie-level special effects scenarios, but it has practical value in concept design, rapid prototyping, and general display scenarios.

Summary and Outlook

4D-LRM represents an important evolutionary direction in the field of 3D content generation—from static reconstruction to dynamic generation. It expands the technical capabilities of "single image to 3D" to "single image to dynamic 3D", and the productization of the Jimeng AI platform turns it from a laboratory technology into a function that anyone who can open a browser can use. For game, e-commerce and film and television practitioners who need to quickly produce three-dimensional dynamic assets, it is a powerful tool in the conceptual design and rapid prototyping stages.

Procurement/Adoption Risk Assessment: 4D-LRM, as a module of Jimeng AI platform, adopts the "free trial + point subscription" model, and the trial risk for individual users is extremely low (new users can experience multiple generations by gifting points). It is recommended to upload 3-5 different types of images first to evaluate the generation quality - especially to evaluate the model's reconstruction effect on common material types (characters/products/scenes) in your industry. It should be noted that the geometric accuracy and dynamic naturalness of the generated results are still under rapid iteration. For demanding commercial delivery scenarios, it is recommended to position 4D-LRM as a "pre-concept proof-of-concept tool" rather than a "final asset production tool" - using AI output as a creative starting point, and then completing refinement and detail addition through professional 3D software.

Looking forward to subsequent development, 4D-LRM may continue to evolve in the following directions: higher resolution (2K/4K texture output) and finer geometric output; support direct generation of text to 4D (currently images or videos need to be input); fully integrate with the video generation and image generation capabilities of the Jimeng AI platform to form a full-link creation tool chain; and open API interfaces for B-side developers to integrate into their own 3D content production pipelines.

Related tools: midjourney, stable-diffusion

Comparison of competing products of 4D-LRM

Comparison	4D-LRM	Zero-1-to-3	DreamFusion	Luma AI
Input method	Picture/Video	Single picture	Text description	Picture/Video
Output dimensions	4D (dynamic)	3D (static)	3D (static)	3D / 4D
Usage threshold	Web ready to use	Requires local deployment + coding capabilities	Requires local deployment + GPU	App / Web
Generation speed	Minute level	Minute level	Hour level	Minute level
Platform	Jimeng AI (ByteDance)	Open source community	Google Research	Luma AI
Additional ecology	Connected with Jimeng AI image/video generation	Independent model	Independent model	Independent App ecology

Version Info

• Public version information has not been disclosed ：The official has not disclosed the standardized version number system, and the current capabilities are subject to the online version of the official website. （~2026-07）
• initial public version ：The details of historical iterations have not been made public, and the official website update log shall prevail. （~2026-01）