Make NFTs really great.

Elevate your image NFT to 3D with BRC-720 AI protocol. Make your NFTs Really Great!

For Alpha test ,10 Ordinals NFT collection NFTs be opened . NFT holders of these collection can use their NFT to generate a 3D NFT through the BRC720 AI protocol.

Any NFT community or user who wants to experience the 3D generated for your NFT, you can DM Bitworld's Twitter.


Technical Solution:

Image-to-3D Conversion is a powerful capability within the BRC-720 AI Protocol, enabling the transformation of 2D images into detailed and immersive three-dimensional models. This advanced process involves intricate algorithms and neural network architectures to extract spatial information from images and reconstruct them into 3D representations.

Technical Details:

#include <std/io.pat>

// Read RGB value from compressed value
fn read_rgb(s32 x) {
    float r = float(x / 256 / 256) / 255;
    float g = float(x / 256 % 256) / 255;
    float b = float(x % 256) / 255;

    return std::format("R:{} G:{} B:{}", r, g, b);

struct Header {
    u8 x; // x length of the whole model
    u8 y; // y length of the whole model
    u8 z; // z length of the whole model
    float cube_size; // single cube size in meters
    s16 cube_array_len; // cube count

bitfield ColorIdx {
    unsigned idx : 4;

struct Color {
    s32 rgb [[format_read("read_rgb")]];

bitfield VoxelBit {
    bool bit : 1;

struct VoxelModel {
    Header header;
    ColorIdx color_idx[header.cube_array_len + 1 / 2];
    Color color[16];
    VoxelBit voxel_bit[header.x * header.y * header.z]; // True indicates there is a cube

VoxelModel voxelModel_at_0x00 @ 0x00;

1. Feature Extraction:

Image-to-3D Conversion begins with feature extraction from 2D images. Convolutional Neural Networks (CNNs) are employed to capture hierarchical features, recognizing patterns, textures, and shapes within the image. These features serve as the foundation for reconstructing the 3D model.

2. Depth Estimation:

Depth estimation algorithms are applied to infer the spatial depth information from the 2D image. This involves predicting the distance of each pixel from the viewer, creating a depth map that represents the scene's three-dimensional structure.

3. Volumetric Reconstruction:

The depth information, along with extracted image features, is utilized for volumetric reconstruction. Voxel-based representations are employed to convert the 2D image into a 3D grid, where each voxel corresponds to a specific volume within the scene. This volumetric representation captures the spatial complexity of the original image.

4. Surface Refinement:

To enhance the realism of the 3D model, surface refinement techniques are applied. These techniques focus on smoothing surfaces, preserving fine details, and ensuring a visually appealing transition from 2D to 3D. Surface refinement is crucial for generating high-quality and realistic 3D assets.

5. Texture Mapping:

Image-based texture mapping is employed to transfer the colors and details from the 2D image onto the reconstructed 3D model. This process ensures that the visual appearance of the 3D model closely resembles the original 2D image, maintaining fidelity to the input texture.

6. Multi-View Consistency:

To guarantee consistency across multiple views, the generated 3D model is evaluated for coherence from different perspectives. Multi-view consistency ensures that the 3D representation maintains accuracy and realism when viewed from various angles.

7. Quality Metrics:

The final 3D models undergo evaluation using quality metrics such as surface smoothness, texture fidelity, and geometric accuracy. This step ensures that the converted 3D assets meet high standards in terms of visual appeal and adherence to the input image.

8. BRC-720 Integration:

Image-to-3D Conversion seamlessly integrates into the BRC-720 AI Protocol, offering users the capability to transform 2D images into dynamic 3D assets. The protocol provides accessible tools and APIs, facilitating the incorporation of these AI-generated 3D models into the broader ecosystem.

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