GPU-Generated Heart, Ribbon and Particle Effects for a GLB Character on Jetson Orin Nano

Hello NVIDIA Developer Community,

I would like to share a real-time 3D character rendering and visual-effects experiment that I have been developing for the NVIDIA Jetson Orin Nano Super.

The project focuses on rendering a GLB character together with GPU-generated heart-shaped trails, procedural ribbons, particles, crystal fragments and screen-facing impact effects.

The main goal is to explore how multiple animated effect layers can be generated and rendered in real time while keeping CPU involvement relatively low.

Video Demonstration

The following video shows the current rendering result.

The sequence includes several real-time visual-effect stages:

  • Animated heart-shaped energy paths
  • Procedural orbiting ribbons
  • Layered particle trails
  • Wing-like sweeping effects
  • Crystal and polygon-fragment bursts
  • Screen-facing heart impacts
  • Emissive character lighting
  • Multi-stage transitions between idle and effect states

The video was captured at a high resolution, although it should be considered a visual demonstration rather than a formal performance benchmark.

Rendering Architecture

The current implementation separates the character rendering and dynamic effect generation into different stages.

The character is loaded from a GLB asset and rendered through OpenGL.

The dynamic effects are generated from time-dependent parameters such as:

  • Effect phase
  • Character position
  • Camera orientation
  • Curve progress
  • Particle lifetime
  • Rotation angle
  • Impact direction
  • Transition intensity

CUDA is used to update dynamic effect data, while OpenGL performs the final rasterization and compositing.

The CPU is mainly responsible for application control, effect-state selection and synchronization rather than calculating every particle or vertex individually.

Procedural Heart and Ribbon Effects

Several effects in the video are constructed from parametric curves.

A simplified heart-shaped curve can be represented using equations similar to:

x(t) = 16 sin³(t)

y(t) = 13 cos(t) − 5 cos(2t) − 2 cos(3t) − cos(4t)

The resulting curve can then be transformed into:

  • Particle emission paths
  • Ribbon centerlines
  • Expanding heart outlines
  • Screen-facing impact shapes
  • Multi-layer orbital effects

Additional noise, rotation, scaling and time offsets are applied so that multiple layers do not follow exactly the same trajectory.

Dynamic GPU Geometry

The larger ribbon and fragment effects require dynamic geometry to be updated every frame.

The current approach generates or updates data such as:

  • Vertex positions
  • Texture coordinates
  • Particle sizes
  • Opacity values
  • Rotation parameters
  • Emissive intensity
  • Fragment velocities

The generated data is then consumed by the OpenGL rendering stage.

One area I am currently evaluating is whether frequently mapping and unmapping CUDA–OpenGL resources introduces unnecessary synchronization overhead compared with using longer-lived or persistently managed buffers.

Character and Material Rendering

The character uses multiple material regions so that skin, hair, clothing and emissive elements can be handled independently.

The rendering pipeline is being developed around the following material properties:

  • Base color
  • Metallic
  • Roughness
  • Normal mapping
  • Emissive masks
  • Alpha blending
  • Stylized character outlines

Dynamic effect lighting is also used so that the heart and ribbon effects influence the visual appearance of the character.

Transparency and Overdraw

A major challenge in this scene is that many effects are transparent and overlap one another.

This can produce significant overdraw, especially when several large ribbons, particles and screen-facing effects appear simultaneously.

The main optimization areas currently include:

  • Reducing unnecessary transparent layers
  • Limiting the screen coverage of large effects
  • Using lower-resolution intermediate effect buffers
  • Avoiding excessive particle counts
  • Improving alpha-blending order
  • Reducing fragment-shader workload
  • Using adaptive effect quality at high resolutions

Bloom and Exposure

Some of the effects use high emissive intensity to create a soft, luminous appearance.

However, when multiple emissive layers overlap, the result can become excessively bright and lose detail.

I am currently tuning:

  • HDR effect intensity
  • Bloom threshold
  • Exposure
  • Tone mapping
  • Highlight preservation
  • Particle opacity
  • Additive-blending strength

The goal is to retain the bright fantasy style without allowing the heart and crystal effects to become completely white.

Runtime Structure

The application is organized around separate responsibilities for:

  1. Character animation and transforms
  2. CUDA effect-data generation
  3. OpenGL character rendering
  4. Transparent effect rendering
  5. Post-processing and bloom
  6. Timing and effect-state control

The effects are activated through a timeline-based state system, allowing different effect combinations to run in sequence.

This architecture could also be extended to gesture-controlled abilities, interactive characters or real-time game effects.

Current Optimization Goals

My current development focus includes:

  • Reducing transparent-pixel overdraw
  • Improving CUDA–OpenGL synchronization
  • Reducing dynamic vertex-buffer update costs
  • Preserving visual detail at high resolution
  • Improving bloom and exposure stability
  • Separating character and effect rendering workloads
  • Adding adaptive effect quality
  • Profiling GPU synchronization with Nsight Systems

Questions for the Community

I would appreciate feedback on the following technical questions:

  1. For frequently updated procedural effect geometry, is it generally better on Jetson to map CUDA–OpenGL resources each frame, or to use a more persistent buffer-management strategy?

  2. What is the recommended method for reducing transparent-effect overdraw while preserving layered ribbon and particle visuals?

  3. Would packed vertex formats or half-precision effect attributes provide a meaningful bandwidth reduction for this type of workload on Jetson Orin Nano?

  4. Is rendering bloom and other post-processing effects into a reduced-resolution framebuffer generally recommended for maintaining performance at high output resolutions?

  5. Which Nsight Systems markers or profiling methods are most useful for locating synchronization stalls between CUDA effect generation and OpenGL rendering?

Thank you for reading.

Any feedback regarding CUDA–OpenGL interoperability, procedural effect generation, transparent rendering or Jetson-specific optimization would be greatly appreciated.

Hi,
We have the graphics samples:

/usr/src/nvidia/graphics_demos/

Please check it for further development. And there are related sections in developer guide:

Graphics — NVIDIA Jetson Linux Developer Guide
Jetson Software Architecture — NVIDIA Jetson Linux Developer Guide
Tegrastats Utility — NVIDIA Jetson Linux Developer Guide

Thank you for the guidance.

I will review the graphics samples under:

/usr/src/nvidia/graphics_demos/

and compare their EGL/OpenGL initialization, rendering loop, buffer management and resource cleanup with my current implementation.

I will also use tegrastats to collect GPU utilization, memory usage, temperature and power information during the GLB rendering and CUDA-generated effect sequences.

This should help me improve both the graphics architecture and the performance measurements of the project.

Thank you again for pointing me to the relevant Jetson resources.

jetson_cuda_opengl_heart_ribbon_mrs_v1.zip (21.4 KB)

This minimal sample intentionally does not include the original GLB character model.

It isolates the CUDA–OpenGL interoperability path used by the full application:

CUDA-generated ribbon and particle vertices
→ CUDA-mapped OpenGL VBO
→ OpenGL rendering

The GLB loader, character assets, textures, animation system and post-processing
were removed to reduce unrelated dependencies and make the performance behavior
easier to reproduce.